Engineering Applications of Computational Fluid
Mechanics
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tcfm20
Artificial intelligence models for suspended river
sediment prediction: state-of-the art, modeling
framework appraisal, and proposed future
research directions
Hai Tao, Zainab S. Al-Khafaji, Chongchong Qi, Mohammad ZounematKermani, Ozgur Kisi, Tiyasha Tiyasha, Kwok-Wing Chau, Vahid Nourani,
Assefa M. Melesse, Mohamed Elhakeem, Aitazaz Ahsan Farooque, A. Pouyan
Nejadhashemi, Khaled Mohamed Khedher, Omer A. Alawi, Ravinesh C. Deo,
Shamsuddin Shahid, Vijay P. Singh & Zaher Mundher Yaseen
To cite this article: Hai Tao, Zainab S. Al-Khafaji, Chongchong Qi, Mohammad ZounematKermani, Ozgur Kisi, Tiyasha Tiyasha, Kwok-Wing Chau, Vahid Nourani, Assefa M.
Melesse, Mohamed Elhakeem, Aitazaz Ahsan Farooque, A. Pouyan Nejadhashemi, Khaled
Mohamed Khedher, Omer A. Alawi, Ravinesh C. Deo, Shamsuddin Shahid, Vijay P. Singh
& Zaher Mundher Yaseen (2021) Artificial intelligence models for suspended river sediment
prediction: state-of-the art, modeling framework appraisal, and proposed future research
directions, Engineering Applications of Computational Fluid Mechanics, 15:1, 1585-1612, DOI:
10.1080/19942060.2021.1984992
To link to this article: https://doi.org/10.1080/19942060.2021.1984992
© 2021 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
Published online: 28 Oct 2021.
Submit your article to this journal
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=tcfm20
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
2021, VOL. 15, NO. 1, 1585–1612
https://doi.org/10.1080/19942060.2021.1984992
REVIEW ARTICLE
Artificial intelligence models for suspended river sediment prediction:
state-of-the art, modeling framework appraisal, and proposed future research
directions
Hai Taoa,b,c , Zainab S. Al-Khafajid,e , Chongchong Qif , Mohammad Zounemat-Kermani g , Ozgur Kisi h , Tiyasha
Tiyashai , Kwok-Wing Chau j , Vahid Nouranik,l , Assefa M. Melesse m , Mohamed Elhakeem n,o , Aitazaz Ahsan
Farooquep,q , A. Pouyan Nejadhashemir , Khaled Mohamed Khedhers,t , Omer A. Alawiu , Ravinesh C. Deov ,
Shamsuddin Shahid w , Vijay P. Singhx and Zaher Mundher Yaseeny,z
a School of Electronics and Information Engineering, Ankang University, China; b School of Computer Sciences, Baoji University of Arts and
Sciences, China; c Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia;
d Al-Furrat Al-Awsat Distribution Foundation, Ministry of Oil, Babylon, Iraq; e Department of Building and Construction Technical Engineering,
College of Technical Engineering, The Islamic University, Najaf, Iraq; f School of Resources and Safety Engineering, Central South University,
Changsha, China; g Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran; h Faculty of Natural Sciences and
Engineering, Ilia State University, Tbilisi, Georgia; i Faculty of civil engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam;
j Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; k Center of
Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran; l Faculty of Civil and Environmental Engineering,
Near East University, Nicosia, North Cyprus, Turkey; m Department of Earth & Environment, Florida International University, Miami, FL, USA;
n Department of Civil Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi, UAE; o Department of Civil Engineering, College of
Engineering, University of Tennessee, Knoxville, TN, USA; p Faculty of Sustainable Design Engineering, University of Prince Edward Island,
Charlottetown, PE, Canada; q School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PE, Canada;
r Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, USA; s Department of Civil Engineering,
College of Engineering, King Khalid University, Abha, Saudi Arabia; t Department of Civil Engineering, High Institute of Technological Studies,
Mrezgua University Campus, Nabeul, Tunisia; u Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia,
Skudai, Johor Bahru, Malaysia; v School of Sciences, University of Southern Queensland, Springfield, QLD, Australia; w School of Civil Engineering,
Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru, Malaysia; x Department of Biological and Agricultural Engineering
and Zachry, Department of Civil Engineering, Texas A&M University, College Station, TX, USA; y New era and development in civil engineering
research group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq; z College of Creative Design, Asia University, Taichung City, Taiwan
ABSTRACT
ARTICLE HISTORY
River sedimentation is an important indicator for ecological and geomorphological assessments of
soil erosion within any watershed region. Sediment transport in a river basin is therefore a multifaceted field yet being a dynamic task in nature. It is characterized by high stochasticity, non-linearity,
non-stationarity, and feature redundancy. Various artificial intelligence (AI) modeling frameworks
have been introduced to solve river sediment problems. The present survey is designed to provide an updated account of the latest and most relevant AI-based applications for modeling the
sediment transport in river basin systems. The review is established to capture the subsequent developments in the advanced AI models applied for river sediment transport prediction. Also, several
hydrological and environmental aspects are identified and analyzed according to the results produced in those studies. The merits and constraints of the well-established AI models are further
discussed in much detail, particularly considering state-of-the art, modeling frameworks and their
application-specific appraisal, and some of the key proposed future research directions. Together
with the synthesis of such information to drive a new understanding of models and methodologies related to suspended river sediment prediction, this review provides a future research
vision for hydrologists, water scientists, water resource engineers, oceanography and environmental
planners.
Received 9 November 2020
Accepted 21 September 2021
1. Introduction
River systems are often regulated for multipurpose usage,
such as, but not limited to, water supply, irrigation,
navigation, flood control, and hydropower generation
CONTACT Zaher Mundher Yaseen
KEYWORDS
Advanced computer aid;
sediment transport
modeling; artificial
intelligence models;
literature review
(Evaristo & McDonnell, 2019). In addition, river systems
are used for wastewater disposal platforms. For instance,
the drain water from textiles, pharmaceutical, mills and
other factories (Bhagat & Tiyasha, 2013; Bhagat et al.,
zaheryaseen88@gmail.com, yaseen@al-ayen.edu.iq
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
1586
H. TAO ET AL.
2018; Yaseen, Zigale, et al., 2019). Effective utilization
of a river system’s resources and the mitigation of risk
posed to this system usually requires water treatments
with different forms of civil structures and hydrological control systems (Kisi et al., 2019; Yaseen, Mohtar,
et al., 2019). Furthermore, altering the watershed runoff
conditions by increasing the land use intensity for agricultural or recreation activities, and by removing the
vegetation cover for urbanization, building roads and
highways constructions, or mining operations can significantly affect the dynamics and the morphology of a river
system (Heathcote, 2009). When a river system’s equilibrium is disturbed by human-induced activity, it often
tends to adjust to a new equilibrium state by scouring
the bed, depositing the sediments, or changing its overall planform. Such changes may be localized or may also
extend over a long reach. The issues of aggradation and
degradation, siltation of reservoirs, channel scours during a flood event, local scour around the structures, and
river bend migration, are the examples of such changes
to a natural river system. Excessive amount of sediment
in water creates problems for the operation of hydraulic
machinery (Betrie et al., 2011; Walling & Collins, 2008).
Large concentration of sediment also affects the overall quality of water. Therefore, addressing such problems
requires an prediction of suspended loads, which represent about 95% of the total sediment loads (Simons
& Şentürk, 1992). Development of a reliable suspended
sediment transport model still remains a challenge due
to the complex character of a river system’s geometry
that governs the water velocity, and the turbulence structure of flow, which in turn, control the sediment-carrying
capacity of the water flowing through the river system
(Armanini et al., 2015).
Despite the importance of sediment transport in environmental pollution, its pattern understanding from the
engineering point of view is less than satisfactory, perhaps due to the complex interrelations of a large number
of variables that influence the transport process (Shojaeezadeh et al., 2018). Traditionally, hydrologic studies
have relied on similitude analysis through experiments,
because there appear to be no reliable and comprehensive theoretical formulae that can describe the two-phase
phenomenon of fluid and sediment transport. Numerous analytical and experimental methods were developed
to compute sediment transport variables. Some of these
methods describe the geometric boundary and its resistance to water flow, sediment transport rate, and mass
conservation of sediment. This article aims to review
some of the useful and practical artificial intelligence
(AI) models that have been applied to model suspended
sediment in a river system. The AI-based models are
described in the next section after reviewing the relevant
literature related to suspended sediment transport.
Accurate prediction of the amount of suspended sediment in rivers and streams is critically important for the
operation of canals, diversions, and dams (i.e. hydraulic
structures) (Cigizoglu, 2004; Liu, Zhou, et al., 2019;
Sharafati et al., 2019; Suif et al., 2016). In watershed
systems, sediment transport and erosion are a complex
hydrological and environmental problems so the impact
of sediments present in a river in terms of the global utilization of surface water resources has become a major
research area (Greig et al., 2005; Malagó et al., 2017; Sinha
et al., 2019). Several natural processes influence sediment dynamics in river basins, including deforestation,
overgrazing, and agricultural activities that erode the
soil surface and contribute much of the sediment input.
Given the difficulty faced by physical-deterministic models (e.g. issues associated with initial or boundary conditions, stochasticity of river flow, and non-stationarity
of the flow), the prediction of sediment load in a river
is more likely to be achieved using AI-based modeling
frameworks that are capable of handling nonlinear relationships between water flow and environmental factors.
It is of prime interest to the present discussion that
we consider suspended sediment prediction as a complex
and nonlinear process, given the several influencing factors (Bhagat, Tung, and Yaseen, 2020). In general, these
factors, which are likely to drive our understanding of the
causal inference that exists between sediment transport
and its related predictor variables, could be classified into
four primary categories as Afan et al. (2016):
(i) Meteorological origin (such as precipitation characteristics and erosive effect of rainfall).
(ii) Hydrological origin (such as base flow, runoff
amount, and floodwater rates).
(iii) Geological origin (such as river basins’ soil characteristics).
(iv) Watershed geomorphology (drainage pattern, topography, and hypsometry).
Due to its interdisciplinary nature, sediment transport remains a widely explored research area, spanning
across several methods that have been developed for the
prediction (estimation) and the forecasting of suspended
sediment in rivers (Williams & Berndt, 1976). However,
the adoption of purely physical or deterministic methods
can be inaccurate or rather time-consuming (particularly
incorporating the initial or boundary conditions into the
physical-based models that differ significantly for different watersheds). Furthermore, such methods can be overparameterized due to the nonlinear and complicated
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
nature of suspended sediment transport in river bodies.
Even though such spatial data can be obtained from satellite or other remotely sensed sources, they are difficult to
obtain at the precise level of initial condition for different watersheds and also, they may need calibration before
they are ready to be used as a physical model input (Akay
et al., 2008).
The determination of physical processes involved in
the transportation of suspended sediment to water bodies
is important for the practical implementation and putting
in place the necessary measures to mitigate sediment
deposition (Adams et al., 2018; Sadeghi & Singh, 2017).
Several studies have recently reported the issues associated with active storage and estimation of a reservoir’s
lifespan (De Vente et al., 2005). The issue of reservoir sedimentation is thus a universal problem, which, if solved,
can be of universal value. Another issue is the progression
of reservoir sedimentation that is generally is a complex
transport mechanism (Verstraeten et al., 2003).
Various studies have been shown that the complexity
of physical processes related to the current-flow density
can contribute to the difficulty in predicting the concentration of suspended sediment (Leisenring & Moradkhani, 2012; Wu et al., 2018). The density current-flow
refers to the specific flow pattern experienced. This can
happen when the water-specific gravity of the inflow turbidity is greater than that of water in the reservoir (Wang
et al., 2019). The water of high relative density settles at
the base of the reservoir and maintains its flow under relatively clean water. However, there is a clear demarcation
between the two fluids of varying densities. A conceptual
model’s capability to represent reality is a function of its
conceptual background; hence, it is necessary to ensure
that such models are built using out-of-range data. Thus,
the dynamics of density current flow can be established
using a conceptual model developed with reliable data in
the first stage.
Water pollution is another sedimentation-related
problem of interest to the present study (Tiyasha &
Yaseen, 2020). Soil degradation by water erosion causes
the transportation of pollutants from the soil surfaces
into the river bodies (Halecki et al., 2018; Zhao et al.,
2017). Numerous models and empirical equations have
been developed for soil losses estimation, especially to
estimate the size distribution of sediments leaving the
field, e.g. empirical equations and physically based models (Rice & Church, 1996). The physically-based models
usually operate with a high quality, and huge data volume,
which sometimes may be greater than the actual available
amount of data in the study area. These data are often
required to develop the empirical relationship(s) between
the watershed and relevant predictors, and they must
be fed as initial conditions, or empirical constants into
1587
the physics-based equations for model validation purposes. On the other hand, the composition of sedimentassociated with individual events cannot be predicted by
such empirical equations, prompting the need for the
often unavailable data (Cigizoglu & Alp, 2006).
Suspended sediment load (SSL) has been modeled
using several methods like empirical or AI models
(such as those using data-driven approaches), hydraulic/
numerical methods (such as sediment transport or
physics-based models), statistics-based models (such as
copula function or joint distribution models), as well
as physical/mathematical models (such as distributed
physically-based and lumped conceptual models) (Ab
Ghani & Azamathulla, 2014; Merkhali et al., 2015). In
this respect, some studies have attempted to analyze the
pattern of SSL in stream flow by using one factor such
as high flow and a flood event during individual hydrologic events, and these have been achieved mainly by
the use of mathematical models (Duy Vinh et al., 2016;
Fang & Wang, 2000; Huang et al., 2015; Lenzi & Marchi,
2000). These studies have reported that the relationship
between SSL and the flow rate is not so robust, and that it
is likely to follow a non-constant (i.e. non-linear) trend.
Furthermore, most of these studies have pointed out a
lag between the peak of SSL and that of the flow rate.
Regarding the hydraulic/numerical models, these tend to
be time-consuming and rather complicated because they
involve solving a set of differential equations in 2-phase
stream flow discharge and sediment transport (Demirci
& Baltaci, 2013; Jha & Bombardelli, 2011). Mathematical
models may not work favorably, unless the true spatial
distribution of the data for most of the important variables e.g. evaporation and precipitation are provided and
are of relatively high quality. They also require data on
the spatial changes in watershed properties; hence, such
models involve a prolonged modeling process. Owing to
the unavailability of required data in most developing
countries, the results deduced from physical and mathematical models are often uncertain and can be impractical for diverse watershed regions (Afan et al., 2016).
Traditionally, either simple statistical models (e.g. sediment rating curves (SRC)) or numerical models (e.g.
finite difference methods) have been employed to simulate the behavior of SSL in rivers or streams (Nguyen et al.,
2009; Walling, 1977). Recently, the emergence of artificial intelligence (AI) or machine learning (ML) models, that operate by the integration of soft computing
methods and data mining approaches, have led to many
promising results, especially in simulating nonlinear systems related to hydrological procedures to solve water
resources problems (Qin et al., 2019; Yaseen, Ebtehaj,
et al., 2019; Zounemat-Kermani et al., 2009). Considering recent developments in artificial intelligence models
1588
H. TAO ET AL.
Figure 1. Frequently used keywords on river sediment prediction employing artificial intelligence models and major countries undertaking the works.
and their implementations in modeling sediment transport in river basins, it is necessary to therefore create new
grounds that can advance the past and current progress
on AI-based models.
The diverse nature of independent parameters required
to model sediment problems as well as the complicated
nonlinear process of SSL have provided motivation to
explore the capability and efficiency of AI-based techniques for SSL modeling (Francke et al., 2008; Olyaie
et al., 2015; Shamaei & Kaedi, 2016). To date, several
supervised learning, AI-approaches have been developed
for the modeling of SSL. The frequently used keywords
on river sediment prediction that employed AI-models
and the countries where these works were undertaken
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
1589
Figure 2. Artificial intelligence models applied for sediment concentration prediction.
are reported in Figure 1. Determined from the literature,
these are based on the works reported in Scopus database
with Figure 2 presenting the implemented models as
being:
(i) Network-based AI models include algorithms, such as:
• Artificial Neural Network (ANN) (Alp & Cigizoglu, 2007).
• Adaptive neuro-fuzzy inference system (ANFIS)
(Kisi & Zounemat-Kermani, 2016).
• Wavelet transformation (Rajaee, 2011).
• Bayesian network (Mount & Stott, 2008).
(ii) Tree-based AI models include algorithms, such as:
• Classification and regression trees (CART)
(Choubin et al., 2018).
• Multivariate adaptive regression splines (MARS)
(Yilmaz et al., 2018).
• M5 model tree (Senthil Kumar et al., 2012).
(iii) Support vector-based AI models include algorithms,
such as:
• Support vector machines (SVM)
• Support vector regression models (SVR)
(Zounemat-Kermani et al., 2016).
(iv) Evolutionary AI models include algorithms, such as
• Gene expression programming (GEP) (Azamathulla
et al., 2012).
• Genetic programming (GP) (Kisi et al., 2012).
• Evolutionary fuzzy inference system (Kişi, 2009).
The current study is aimed to survey the recent literature on the applicability of AI methods for sediment transport modeling, particularly over the past four
years, with the primary aim of providing a best practice,
summarized guideline on the application and enhanced
capability of AI models. The survey also highlights the
progress made in terms of AI methods and related
advanced computer-aided methodologies for modeling
river sediment transport. The review also discusses each
of the AI model’s merits and constraints, the necessity
for additional exploration of various river types, their
unresolved issues related to data collection, and recommendations for future research. Since previous studies
embarked on an extensive review of the AI models (Afan
et al., 2016), this study serves as an extension recognizing
recent advancements in the deployment of AI models as
prediction tools. We also aim to make recommendations
for novel approaches that demonstrate the versatility of
AI models in river systems engineering, water resources
management and sustainability.
2. Classical (standalone) artificial intelligence
models
2.1. Introduction of classical AI models
2.1.1. Artificial neural network
Artificial neural network is a powerful computation technique that can handle non-linear relationships and complex problems (Qin, Wang, Lin, Zhang, Xia, et al., 2018).
ANN models have been applied in many areas, such as
speech and image recognition, chemical research, medicinal and molecular biology research, and ecological and
environmental studies (Lek & Guégan, 1999). ANN models were developed, considering the problem at hand and
the solution to achieve the goal (Qin, Wang, Lin, Zhang,
and Bilal, 2018). An ANN architecture consists of three
layers, the first one is called the input layer where computation of weighted sum is performed, the second layer
is used for data processing i.e. hidden layer, which can be
converted to multiple layers depending on the complexity of the problem, and lastly, the final result is produced
in the output layer. Each layer is made up of neurons
which unite to form the architectural framework (Singh
et al., 2009). Conventional ANN models typically consist
of multi-layer feed-forward neural network with application of backpropagation algorithm (BPNN) (Rumelhart
et al., 1988); similarly, radial basis function neural network (RBFNN) applies feed-forward neural network and
one hidden layer for model execution (Chen & Cowan,
1991), multi-layer perceptron (MLP) network using feedforward neural network (Rosenblatt, 1961), recurrent
neural network (RNN) using backpropagation and additional layer connected to hidden layer (Du & Swamy,
2019), Levenberg-Marquart (LM), Bayesian regularization (BR), and gradient descent and adaptive learning
1590
H. TAO ET AL.
Figure 3. Typical ANN architecture for SSL modeling where w: weight and bias are justified by the training process. Most common
transfer functions are sigmoid, Gaussian and tangent sigmoid (i.e. tansig).
(Maier & Dandy, 1999; Samarasinghe, 2006). The general
architecture of three layers is presented in Figure 3.
2.1.2. Support vector machine
SVM, based on statistical learning theory, has a resilient
conjectural statistical arrangement which makes it a
robust model. SVM has been successful in handling classification, regression and other kinds of forecasting or
predictive modeling. The SVM model is based on statistical learning theory and structural risk minimization
hypothesis. It was developed by (Cortes & Vapnik, 1995)
and since it is a kernel-based model, it is able to reduce
model’s complexity and prediction error. Usually, kernel type models have good adaptability, can achieve great
results in global optimization and generalization, and
are capable of managing small samples and minimizing
empirical risk (Raghavendra & Deka, 2014). Kernels are
special nonlinear functions created by non-linear mapping and allow the model to separate complicated hyperplanes. The correct selection of a kernel function is the
key to produce excellent model performance. The most
applied kernel functions are linear, polynomial, radial
basis function, and sigmoid. The modified versions popularly applied are least-square SVM (Suykens & Vandewalle, 1999), linear programming SVM (Zhou et al.,
2002), and Nu-SVM (Schölkopf et al., 2000). The general
architecture of the SVM model is presented in Figure 4.
2.1.3. Adaptive neuro-fuzzy inference system
Fuzzy logic models are a powerful tool for dealing with difficult computational problems (Wang, Kisi,
Zounemat-Kermani, Zhu, et al., 2017), and can deal with
non-linearity, uncertainty, and subjective data. These
models overcome the shortcomings of numerical modeling. The most popularly applied model is ANFIS, whose
architecture consists of a multilayer feed-forward network that utilizes a neural network learning algorithm
and can identify non-linear boundaries and fuzzy logic to
distinguish non-linear equations and together can map
the input-output space (Takagi & Hayashi, 1988) which
allows it to achieve high non-linear mapping for nonlinear time series data. The stages of ANFIS consist of
choosing the type of interference system, such as Mamdani, Sugeno and Tsumoto (Mamdani & Assilian, 1975;
Takagi & Sugeno, 1985), aggregation, and defuzzification.
The general five-layer design of ANFIS is presented in
Figure 5.
2.1.4. Bayesian networks (BNs)
Bayesian methods provide conformity for reasoning in
terms of the fractional beliefs under the condition of
uncertainty. Various numerical parameters imply the
degree of belief and later they parameters are combined
and manipulated as per the rules of probability theory,
considering conditional probabilities, absolute certainty,
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
1591
Figure 4. (a) Typical SVM architecture for SSL modeling where α = weight and b = bias. Most common transfer functions are linear,
polynomial, radial basis, and sigmoidal, K = support vector, m = number of support vectors, ϕ(x) = non-linear function. (b). Non-linear
SVR vapnik’s ǫ-insensitivity loss function.
and conditionally independent variables (Pearl, 2014).
They give rise to the conjugate form of Bayesian theorem
and graphical theory, which adhere to probability rules
for conducting interference. Thus, in a simple form, BNs
are probabilistic, directed acyclic graphical models (Gregory, 2005). For experimental data analysis, a conditional
distribution is computed using the rearrangement of the
chain rule which allows for the computation of conditional probability of the discrete state of any variable with
any number of parents. Additionally, nodes stipulate no
parents, indicating that random variables are not conditionally dependent, and the joint nodes are conditionally
dependent which can be calculated by a joint probability
distribution which is the product of nodes and parents.
States of nodes and their conditional probability can be
allotted as per a qualitative or quantitative procedure. The
efficient BNs are as good as the proximity of dependencies, thus only the most relative number of variables and
1592
H. TAO ET AL.
Figure 5. Five layers ANFIS model design with most common MFs, where, θi1 = Node Output, i = node, F = final output, X1 and
X2 = Input variable, P1, P2, Q1, Q2 = Fuzzy rules.
2.2. Bibliography
Figure 6. Basic Bayesian network design that satisfies the local
Markov property which states that a node conditionally independent of its non-descendants’ parent (e.g. E & F). Joint probability
(e.g. P(G|F), product of P (node|parental node)) is calculated using
the chain rule of probability.
their association with the problem should be used for
construction (Mount & Stott, 2008). Consequently, the
computational cost and complexity can increase exponentially, if the number of nodes is not controlled which
may happen in real-life problems. A schematic representation of a basic Bayesian network design is shown in
Figure 6.
Three AI models, including SVR, ANFIS and ANN models, were employed for SSL simulation at Coruh River,
Turkey (Buyukyildiz & Kumcu, 2017). The models were
built using a daily scale of discharge (Q) and SSL hydrological data, and the accuracy of prediction by the SVM
model over other implemented AI models was confirmed.
Kaveh et al. (2017) examined different trained ANFIS
models with parametric algorithms, such as LevenbergMarquardt and back-propagation. They used the daily
scale of Q and SSL dataset gathered from Schuylkill River,
United States and results showed the capability of the
applied AI models for modeling suspended sediment
load.
Bharti et al. (2017) developed an ANN, least support
vector regression (LSSVR), reduced error pruning tree
(REPT), and M5 Tree model for the modeling of Q and
SSL at the Pokhariys watershed, India. Using climatological variables with monthly scale, including rainfall,
air temperature (AT), relative humidity (RH), pan evaporation (ETo ), solar radiation, sunshine duration, and
wind speed, they showed the influence of climatological variables on the modeled Q and SSL. However, the
performance of AI models was related to the variance
results.
Pektas and Cigizoglu (2017a) presented a traditional
perdition investigation for daily scale sediment transport in Yadkin River using the feasibility of ANN and
MLR models. The ANN model reported an acceptable
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
prediction performance. The same case study was
investigated for ANN internal tuning (Pektas & Cigizoglu, 2017a, 2017b). On the same trend, ANFIS and
ANN models were implemented for sediment prediction (Riahi-Madvar & Seifi, 2018). Predictive models
constructed, based on hydraulic parameters, including
hydraulic radius, water surface slope, sediment particle
dimensions, sediment shear, river discharge, and water
depth. From statistical results and uncertainty analysis,
the ANFIS model showed better results than the ANN
model. The ANFIS and ANN models were developed
for simulating suspended sediment concentration for
Thames River, London, Ontario, using daily water temperature, river discharge, and electrical conductivity, and
results evidenced the potential of ANN in modeling SSC.
Moeeni and Bonakdari (2018) combined the integrated autoregressive moving average exogenous
(ARMAX) model with ANN to predict SSL based on river
Q and SSL using daily time scale. Normalizing the data
using the exponential and Box–Cox transformations,
they found the combined model to have an excellent predictive performance. Hamaamin et al. (2018) designed
two AI models, including ANFIS and Bayesian regression
(BR), to predict SSL for Saginaw River, United States, with
daily data of air temperature, rainfall, and SSL as input
variables. The models were validated against the soil and
water assessment tool (SWAT) and both models provided
a reliable alternative for SWAT to predict SSL.
Gholami et al. (2018) applied the ANN model to evaluate soil erosion from Kaslilian watershed, Iran. The
Geographic information system (GIS) was used for data
pre-processing of the spatial variation of soil erosion,
where the predictors were rainfall intensity and amount,
soil moisture, vegetation cover, slope, air, and soil temperature. The ANN model performed well for the watershed.
The classification and regression tree (CART), adaptive neuro-fuzzy inference system, multi-layer perceptron, and two support vector regression models were
compared for simulating SSL of Haraz watershed in
the northern region of Iran (Choubin et al., 2018).
These models were constructed, based on several hydrometeorological data (e.g. river water level, river discharge,
rainfall, and SSL). The CART model was found to be
superior, especially for one-month lead time.
Samet et al. (2019) predicted SSL in Gizlarchay River,
Maku Dam, Iran, using ANFIS, ANN, and GP using
water temperature, river discharge, and three-section
sediment sampling (CM) and found ANFIS to be far
better than ANN and GP.
Khan et al. (2019) predicted SSC of the Ramaganga
River, India, using the ANN model and with daily river
discharge and suspended sediment concentration. The
SSC prediction was promising for the watershed.
1593
Emamgholizadeh and Demneh (2019) used daily data
of Q and SSL from Telar and Kasilian Rivers, Iran, to
construct several AI models (i.e. GEP, ANN and ANFIS)
which were validated against the SRC approach. The
evolutionary GEP model showed potential for modeling
SSL.
Bisoyi et al. (2019) employed the ANN model to predict SSL for Narmada River, India, using daily rainfall,
Q and SSL data. Considering various hydrological variables and focusing on one peak event based on monsoon
season, it was necessary to use rainfall and Q datasets.
Proper input selection is essential for predictive models (Noori et al., 2011). Kumar et al. (2019) used ANN
and ANFIS along with Gamma Test (GT) for input selection for modeling river sediment and discharge. Daily
antecedent Q and SSL from Pathagudem and Polavaram
watersheds in India for the period 1996–2010 were used.
Results showed the suitability of GT as a prior stage for
the learning process.
Singh et al. (2018) integrated GT and correlation function (CF) with AI models, including multi-layer perceptron (MLP), co-active adaptive inference system (CANFIS), self-organization map (SOM), and radial basis function (RBF), and two regression models (e.g. SRC and
MLR). Models were constructed for hydrological data
obtained from Burhabalang basin, Orissa, India. Both GT
and CF were used to select the appropriate input variables
as a prior modeling procedure. The MLP model with one
lead time of SSL and Q was the best prediction model
among all other models.
Malik et al. (2019) used the RBF, SOM, least square
support vector regression, and MARS models with GT for
input selection to predict SSC from the Ashti, Tekra and
Bamini stations located at Godavari River basin, India.
They found the RBF model to be the best model.
3. Complementary artificial intelligence models
3.1. Wavelet transform
The limitation of various AI models to handle nonstationary data has led to incorporate wavelet transform, developed by (Grossmann Jean, 1984), as s signal preprocessing method to provide insights into timescaling and its relationship through numerical analysis and manipulation of multi-dimensional signals. This
technique can be used for diagnostic classification and
forecasting (Nourani et al., 2014). The hybrid model
applies the wavelet transform, however, its performance
is based on the selection of a suitable mother wavelet and
decomposition level. Frequently used mother wavelets
are Daubechies-1, Daubechies-2, Daubechies-4, Haar,
db2 and db4. The two common forms of wavelet analysis
1594
H. TAO ET AL.
Figure 7. Schematic diagram of hybrid wavelet-AI model: regularity condition with order N decides the choice of wavelet. Wavelet denoising (WDT) is applied to adjust detail coefficient. X1 and X2 = Input variables, D1-Dn = decomposition level, Y = Output variable.
are discrete wavelet analysis which deals with discrete
signals and decomposes the series into sub-signals at a
specific wavelet and decomposition level (Daubechies,
1988). The other is continuous wavelet transform which
deals with continuous signals and is useful for disclosing
series features under multi-temporal scales (Percival &
Walden, 2000). Wavelet transform is applied for wavelet
decomposition, wavelet de-noising, wavelet aided complexity explanation, and wavelet aided predicting (Sang,
2013). A schematic diagram of hybrid wavelet-AI model
is shown in Figure 7.
3.2. Bibliography
A complementary predictive model, based on the integration of data-mining M5 decision tree (M5Tree) with
wavelet data pre-processing technique, was developed for
SSL prediction for the upper Rio Grande and Lighvanchai Rivers (Nourani et al., 2019). Discharge and sediment data with daily and monthly time scales were used
to build the predictive models, and Standalone M5Tree
and ANN models were used for comparison. The complementary WA-M5Tree was found to be a robust prediction model for both rivers and time scales. (Sharghi,
Nourani, Najafi, and Soleimani, 2019) coupled complementary wavelet exponential smoothing (WES) with
ANN model to enhance predictive performance based on
time series data pre-processing as a prior stage to prediction. WES-ANN was validated against autoregressive
integrated moving average (ARIMA), seasonal ARIMA
(SARIMA), and ANN models. The Upper Rio Grande
and Lighvanchai Rivers daily SSL data were used to build
the complementary predictive and benchmark models.
The proposed model exhibited improved prediction.
Himanshu et al. (2017) evaluated the wavelet SVM
(W-SVM) and SVM models for daily SSL prediction for
different lead times (1, 3, and 6 days), using the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite
Precipitation Analysis (TMPA). The WSVM model was
found to be applicable for the prediction of SSL over
all targeted leads, and results showed the bias of TMPA
precipitation on SSL modeling for data-sparse regions.
Alizadeh et al. (2017) equated the W-ANN model and
classical ANN model for predicting multiple scale ahead
of SSC at Skagit River, United States. Daily Q and SSC
data were used for initiating the models based on the
correlated lead times. Results indicated that the W-ANN
model was superior to the classical ANN model.
Sharghi, Nourani, Najafi, and Gokcekus (2019a)
developed a complementary model based on waveletemotional neural network (W-ENN) for predicting daily
and monthly SSL in Rio Grande and Lighvanchai Rivers.
The motivation of the wavelet preprocessing approach
application to establish the fact that sediment transport is
related with stochasticity, seasonality, and non-linearity.
The proposed W-ENN model was validated against standalone ENN and ANN models and was found to be
having potential for modeling SSL.
Himanshu et al. (2016) used an ensemble WA-SVR
model with hydrometeorological variables for the prediction of SSL, including the peak values of sediment and
accumulated sediment for reservoir operation, at multiple steps ahead daily scales, including (1-, 3-, 6- and
9-days). Daily Q, rainfall, and SSL data over 40-years
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
from two watersheds (i.e. Muneru and Marol) in India
were used.
Liu, Zhang, et al. (2019) used the Hilbert-Huang transform (HHT) to identify the time scale of daily horizon
Q, RF, SSC and normalized difference vegetation index
(NDVI) of Kuye River, China. Several predictive models,
including ANN, MLR, and their complementary version
integrated with ensemble empirical mode decomposition
(EEMD), were employed. The intrinsic mode function
(IMF) was used to detect the correlation between RF,
Q, NDVI, and SSC. The integrated EEMD-ANN model
was more accurate than MLR, ANN and EEMD-MLR.
The non-linear IMF provided antecedent values of the
predictors.
4. Nature-inspired hybrid artificial intelligence
models
4.1. Introduction to nature-inspired optimization
algorithm
Nature-inspired meta-heuristic algorithms have gained
popularity in engineering applications because of their
ability to solve optimization problems. They mimic biological incidents, attend local optima, and are flexible.
They can be divided into evolutionary-based, physicallybased, and swarm-based. Evolutionary-based methods
are inspired by the laws of evolution. They allow optimizing by selecting the best individual which leads to the
better next generation. Some of the popular evolutionary algorithms are genetic algorithm (Goldberg & Holland, 1988), evolutionary strategies (Rechenberg, 1994),
genetic programming (Koza, 1994), and gene expression programming (Ferreira, 2001). The swarm-based
method is based on the social behavior of groups of animals and insects. The most popular optimizers are (i)
Particle Swarm Optimization (PSO) found in the social
behavior of flocking birds where particles fly in the search
space to find the best result (Kennedy & Eberhart, 1995);
(ii) ant colony optimization centered around the social
behavior of ants in the ant colony to find the shortest
path to home and food (Dorigo et al., 1996); and Artificial Bee Colony (ABC) inspired by the behavior of honey
bee swarms (Karaboga & Basturk, 2007). In general, the
nature-inspired/heuristic algorithms can be combined
with AI models to serve as efficient optimization algorithms with the potential to improve their accuracy and
precision (Cicek & Ozturk, 2021; Fadaee et al., 2020).
The combined models may be called integrative or hybrid
AI models. In this study, we use the term hybrid for an
embedded AI model with a nature-inspired or heuristic
algorithm that improves the performance of a standalone
AI model.
1595
4.2. Bibliography
Adib and Mahmoodi (2017) used the hybrid (integrative)
ANN-GA model to predict annual SSL in Marun River,
Iran. Zounemat-Kermani (2017) tested several AI models (i.e. ANN, ANN-PSO, ANFIS and GEP) for modeling
SSC in highly dynamic river discharge, and sufficient lead
times were computed using mutual information. Daily
sediment data was gathered from San Joaquin River,
United States. The hybrid ANN-PSO was more efficient
and robust for SSC prediction than ANN, ANFIS and
GEP.
A new AI model, integrating genetic algorithm with
SVR model, was developed for modeling daily scale SSL
at two earth dams located in Iran (Rahgoshay et al., 2018).
Two AI models, including multivariate adaptive regression spline (MARS) and M5Tree, were developed. Results
concluded the superiority of MARS and M5Tree models.
The river discharge with three-day antecedent values had
a major correlation to predict one day ahead SSL.
Integrating the continuity equation and fuzzy patternrecognition into a structure of double artificial neural
networks, (Chen & Chau, 2016) developed a hybrid double feedforward neural network model for daily SSL estimation, by integrating continuity equation and fuzzy
pattern-recognition into a structure of double ANNs. The
results showed that the hybrid model outperformed other
three benchmarking conventional ML models.
A fuzzy C-means clustering (FCM) approach was
hybridized with an SVR model for internal parameter
optimization and the hybrid model was implemented for
predicting SSL of Sistan River, Iran (Hassanpour et al.,
2019). The proposed hybrid FCM-SVR model was developed using univariate modeling scheme where only daily
SSL data were used. Several benchmark models were
developed, including SRC, ANN, ANFIS and SVR, and
results demonstrated the hybrid FCM-SVR model accurately predicted SSL.
A new hybrid AI model was developed based on a
binary nature-inspired optimization algorithm with feedforward neural network model to quantify the monthly
sediment load of Narmada River, one of India’s largest
rivers (Meshram et al., 2019). Using ten years of Q, RF
and SSL historical data, the model was validated against
the ANFIS model and was found superior to the ANFIS
model and accurately modeled SSL.
Rahgoshay et al. (2019) hybridized SVR model with
two nature inspired optimization algorithms, including
GA and particle swarm optimization (PSO) for SSL prediction. M5 Tree and MARS were used for validation
purposes. For two case studies (i.e. Veynakeh and Royan),
located in Iran, the hybridization of PSO with SVR model
was promising for modeling sediment transport.
1596
H. TAO ET AL.
Yadav et al. (2018) developed two hybrid predictive
models by tuning ANN and SVR models using genetic
algorithm (GA) for modeling SSL using RF, Q, T and SSL
data from Mahanadi River, India. Two predictive models,
including MLR and sediment rating curve (SRC), were
used for model assessment. The hybrid models exhibited
a noticeable prediction accuracy.
5.2. Bibliography
Shamaei and Kaedi (2016) applied the ensemble stacking
method to predict SSL based on the results of two different AI techniques, including genetic programming and
neuro-fuzzy models. It was found that the ensemble technique drastically improved the prediction accuracy of the
single soft computing models.
6. Other applied artificial intelligence models
5. Ensemble artificial intelligence models
5.1. Introduction to ensemble artificial intelligence
methods
In contrast to the conventional or stand-alone AI models, in the ensemble learning, multiple base algorithms
are trained. In other words, ensemble learning attempts
to improve the performance of conventional AI models
by combining multiple hypotheses for the same dataset.
Alternatively, ensemble learning can also be accomplished for conjoining the results of different models
(e.g. SVM and ANN) for the same dataset (Wang et al.,
2011). Ensemble methods are learning algorithms that
create classifiers and categorize new data points by considering weighted and unweighted voting of predictions.
The basic method is Bayesian average but newer methods have been developed, such as error-correction output
coding, bagging, and boosting (Dietterich, 2000). Ensemble classifiers are more accurate than any individual
member, and two classifiers producing different errors
are considered as diverse classifiers. Additionally, diverse
classifiers are judged good since one classifier result is
unrelated to the other, giving a better voting advantage
when error rates below 0.5 are chosen. There are three
advantages over other models like ANN or decision tree:
(i) It can produce good accuracy without sufficient data;
(ii) it is constructed by running local search from many
different starting points which provide better approximation resulting in a better computational technique; and
(iii) it has better representation by using effective space
of hypothesis search (Zhang & Ma, 2012).
Random forest (RF) is a bagging type ensemble learning method which has an additional layer of randomness.
It is the combination of predictors creating a distribution pattern of trees in forest and generalizes the error
of tree power of individual trees and correlation among
them. Inputs are selected randomly so that the node can
grow (Bhagat, Pyrgaki, et al., 2021). Its accuracy is good;
it is robust to outliers and noise, faster than bagging
and boosting, and provides good internal assessment of
the error, strength, correlation and variable importance
(Breiman, 2001). The basic architecture RF is presented
in Figure 8.
6.1. Overview of other artificial intelligence models
6.1.1. Classification and regression tree (CART)
Sequential binary splits, applied for explanatory variables
using a decision rule for allocating items to a group, are
the basic concept of a classification tree. CART was initially developed to handle clinical data due to its ability
to simultaneously manage combinations of categorical
variables and continuous information and search for the
best way to split the range of continuous variables into
two groups (Breiman et al., 1984). The tree was able to
split at different points or nodes till the end or leaf node.
The CART addresses linear and regression problems and
helps in linear logistic and additive logistic models for
classification problems. Binary recursive portioning is
used for splitting the sequential data into homogeneous
subsets until the condition is fulfilled. Each split depends
on the definite variable value and divides into two subsets generating a binary tree structure. The construction
features include: selection of binary splits of the measurement space, decision of creating a node or continuous splitting and assigning each terminal node to a
class (Crichton et al., 1997). Classification components
are dependent variables, independent variables, learning
dataset and future dataset. Regression components are
prior probabilities from each outcome and cost matrix.
The progress of CART models has been fairly slow due
to the complexity of analysis in some cases. These models are effective to understand multifaceted interactions
between predictors in respect of traditional multi-variate
techniques. They can handle highly skewed data and categorical data, deal with missing data by using surrogate
variables, need little amount of input for analysis, and are
relatively simple to interpret (Lewis et al., 2000).
6.1.2. Multivariate adaptive regression splines
(MARS)
Multivariate adaptive regression spline is a flexible regression non-parametric modeling approach which can
work with high dimensional non-linear data and is the
form of expansion of spline basis function (Wang, Kisi,
Zounemat-Kermani, and Li, 2017). It utilizes the technique of recursive partitioning approach and regression
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
1597
Figure 8. Random forest (RF).
analysis (Bhagat, Paramasivan, et al., 2021; Friedman,
1991), is flexible and accurate, and can forecast continuous and binary output. It has been applied in the fields of
energy, and environmental and ecological studies and can
interpret complex and nonlinear relationships between
predictors and response variable and does not make any
assumption between input and output variables (Bhagat, Paramasivan, et al., 2021). It consists of knots which
are breaks between regions, basic functions for distinct
intervals of predictors with two phases known as forward
and backward phases. The forward phase generates all
possible basis functions and the backward phase eliminates basis functions which create overfitting which in
turn improves prediction accuracy (Yilmaz et al., 2018).
The general schematic of the MARS model is shown in
Figure 9.
6.1.3. M5 model tree
M5 model tree is a piecewise linear model proposed by
Quinlan (1992). It maps the inputs and the output, constructed in two different stages i.e. the tree growth and
tree pruning. The first stage splits the input into subsets
using a linear regression model and diminishes the error
between measured observations and predicted values and
instantaneously creates decision tree. The second stage
consists of the pruning of tress from each leaf (Heddam
& Kisi, 2018). The basic architecture of M5 model tree is
shown in Figure 10.
6.1.4. Regression model
The regression method is a baseline statistical modeling technique which takes account of the relationship
between variables and finds a meaningful relation and
differences. A regression model can be linear, non-linear,
or parametric and non-parametric, depending on the
type of analysis. The basic models consist of predictor
variables and response variables. The response variables
are assumed continuous, whereas predictors can be discrete or continuous with various assumptions on correlation. There are many frequently applied regression models, such as simple linear regression (Younger, 1979), multiple linear regression (Andrews, 1974), logistic regression (Hosmer et al., 2013), ordinal regression (Harrell,
2015), multinomial regression, and discriminant analysis
(Klecka et al., 1980).
6.2. Bibliography
Khosravi et al. (2018) developed several data mining predictive models to quantify hourly scale SSL measured
1598
H. TAO ET AL.
Figure 9. Multivariate adaptive regression splines (MARS).
Figure 10. M5 model tree.
at Andean Catchment, Chile. Their models were standalone reduced error pruning tree (REPT), instancebased learning (IBK), M5P, hybrid models (i.e. baggingM5P, random sub-space-REPT (RS-REPT), and random
committee-REPT (RC-REPT)). The input combinations
were constructed based on Q, water temperature (WT),
and electrical conductively (EC) in addition to SSL. The
hybrid bagging-M5P data mining model accurately predicted hourly SSL in comparison with other models.
Ulke et al. (2017) inspected five different empirical
models, including models of Einstein, Lane and Kalinske,
Chang-Simons-Richardson, and Brooks, for predicting
SSL of three major rivers in Turkey. The models were formulated using Q, SSL, river cross-section area, material
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
size of the transported material, and Manning coefficient (n). Among the empirical formulations, the Brooks
model attained the best prediction results. However,
when genetic algorithm was integrated with the Brooks
model for internal parameters tuning, the GA-Brooks
model showed results comparable to those attained from
classical ANN and ANFIS models. The empirical model
revealed that the particles size influenced the SSL transport. Overall, the GA-Brooks model exhibited an ability
for modeling SSL of this particular region.
Afan et al. (2017) employed three AI models, including ANN, response surface method (RSM), and RSM
basis global harmony search (GHS), for modeling SSL.
The RSM model is integrated as an input selection
method prior to prediction. Daily Q and SSL over a
decade from Johor River were used to construct the
models. Results showed the viability of integrating RSM
with GHS to select the optimal antecedent records for
prediction.
Kisi and Ozkan (2016) developed a local weighted linear regression (LWLR) model for SSC prediction based
on daily discharge and suspended sediment concentration data in Eel River, United States. LSSVR, ANN and
SRC models were used for validation, and the LWLR
model showed a superior prediction performance.
Using rainfall, river flow discharge, and sediment
yield, Sudhishri et al. (2016) employed a non-linear
dynamic (NLD) model for modeling daily sediment
transport in a large mountainous watershed, Bino Watershed, India. Modeling was done using rainfall, river flow
discharge, and sediment yield. The NLD model showed
superior prediction results over ANN and W-ANN models. However, under-estimation was experienced during
the events of high land-sliding and flash floods.
7. Appraisal of the literature
The exploration of related studies leaves little room for
uncertainty as to the superiority of mathematical datadriven models (viz. regression-based) and soft computing data-driven models (viz. artificial intelligence or
machine learning methods) over traditional sediment
rating curve (SRC) method in simulating SSL (Singh
et al., 2018; Zounemat-Kermani et al., 2016). In a recent
review, Zounemat-Kermani, Matta, et al. (2020) acknowledged the absolute superiority of neuro-computing AI
models over the traditional and statistical methods,
such as SRC, MLR or ARIMA. The findings of studies show that AI models performed better than standard
mathematical data-driven models. However, the question is which category (network-based, tree-based, support vector-based or evolutionary-based) or what type of
AI model can be chosen as the best AI model for SSL
1599
simulation, is still ongoing. Further research in this particular area and related hydrological areas would lead
to an answer but would also make it more indistinguishable and open-ended. Even though the majority of
studies affirm the promising results from AI models in
SSL simulation over traditional methods (e.g. Buyukyildiz & Kumcu, 2017; Choubin et al., 2018; Singh et al.,
2018; Zounemat-Kermani et al., 2016). Different reasons
might be associated with dissimilarities in announcing
the best AI model by various studies. Indeed, existing
contrasts in the datasets applied (e.g. length of dataset,
input vector, and lead time), feature selection methods (e.g. auto-correlation, average mutual information,
Gamma Test), data preparation techniques (e.g. holdout method, cross-validation method), and model tuning
techniques (e.g. learning algorithms, model architecture,
and termination factors) could be pointed out as some
of the leading causes. In such studies (AI application to
SSL), all the details related to modeling (e.g. data division
strategy, used algorithm or method and calibration of its
control parameters) should be provided by the modeler.
In this way, the same methodology can be repeated and
more robust conclusions about the implemented method
may be derived from the results. Wu et al. (2014) suggested a protocol for ANN implementation and evaluated
it using drinking water quality data. They examined 81
journal papers since 2000 and reported that there was no
systematic protocol for the development of ANN models. The proposed protocol included the reasons why a
specific method was selected, methods used and details
of implementation, data collection and pre-processing,
input selection, data splitting, model architecture selection, and model calibration and validation (replicative,
predictive and structural). The study emphasized that
similar protocols should be developed for other AI methods to better follow the implemented methodology for
other case studies.
The literature suggests that the development of most
AI models is based on a multivariate modeling strategy. The suspended sediment is modeled, based on various hydro-meteorological variables, possibly because AI
models can establish the non-linear relationship that
describes the physical progression between the predictand and the predictors by exploiting the advantages
of the randomized learning process. Sediment transport modeling was conducted, using various hydrological
and geophysical variables as causally related predictors
(Tao et al., 2019). For modeling, it is assumed that the
predictor-predictand relationship is stationary, but this
assumption does not hold in real cases, as it depends
on the actual data. When there is a non-stationary
predictor-predictand relationship, the model results can
be uncertain. Hence, the reasons for the non-stationary
1600
H. TAO ET AL.
predictor-predictand relationship must be identified, and
the selection of predictors must be carefully done to
prevent the presence of any non-stationarity.
Different statistical methods have been used to select
the most suitable input combinations to develop sediment load prediction models. These methods are
the gamma test, correlation analysis, intrinsic mode
function, auto-correlation, average mutual information,
neighborhood component analysis, Boruta feature selection, and iterative input selection, to name a few (Ahmed,
Deo, Raj et al., 2021; Ahmed, Deo, Ghahramani et al.,
2021; Kumar et al., 2019; Liu, Zhang, et al., 2019; Singh
et al., 2018). These methods first evaluate each candidate
input’s relation with the output separately and rank them,
and then the higher-ranked inputs are selected for model
development. However, individually highly correlated
inputs do not guarantee good performance when working together due to collinearity. Recursive Feature Elimination (RFE), a wrapper feature selection algorithm, has
shown prominence in selecting ML models’ inputs. The
RFE has been recently used to select inputs for a sediment heavy metal prediction model (Bhagat, Tiyasha,
Awadh, et al., 2020; Bhagat, Tiyasha, Tung, et al., 2020)
and a sediment chemical prediction model (Sakizadeh,
2020). The advantage of RFE is its ability to select the
most suitable input combination by iteratively removing
the less influencing inputs. However, the use of different
kinds of wrappers, including RFE, is still limited in the AI
sediment prediction model development.
Despite the excellent performance of AI models
in modeling sediment transport and abstracting nonlinearities that characterize the physical process via multivariate modeling strategies, it is often necessary to build
new univariate models, which will serve as ‘time-series
prediction models.’ Such models utilize the patterns and
magnitudes of the study variables to predict their potential value in the future. This is important for most developing countries where there are limited or unavailable
hydro-meteorological data at the metrological monitoring stations. Therefore, there is a need to develop such
a univariate modeling strategy for implementation using
the feasibility of soft computing models. It should be
noted that the predictive ability of AI models for univariate input data applications has been verified for sediment
simulation.
The present review also points to the significant
improvement of AI models in their integration with data
pre-processing approaches to achieve a ‘complementary
modeling approach.’ Such studies point to the dependence of integrative models on the input space decomposition with several frequency-based information levels
used to enhance performance. However, according to
a recent study by Quilty and Adamowski (2018), some
of the current research studies inaccurately developed
wavelet-based AI models and thus they could not be
properly applied for practical purposes, mainly related
to forecasting applications. The issues in these research
studies are related to: (i) the potential use of future data
as an input in the development of selected models, (ii)
an inappropriate choice of the decomposition level and
wavelet filters, and (ii) the imprudent partitioning of
training and testing data. Because of not addressing the
reflection boundary conditions in applying the wavelet
decomposition, the researchers have incorrectly implemented wavelet-based AI models, resulting in much better accuracy (with a relative positive bias) than what
is normally achievable. To resolve this issue, the subsequent study of Quilty and Adamowski (2018) reported
a new strategy to avoid such errors and to adequately
using wavelet decomposition method. The strategic recommendation mentioned in the study should be considered in future research studies related to wavelet-based
complementary modeling approach. To demonstrate the
newly proposed approach, a few other research studies (e.g. (Al-Musaylh et al., 2020; Ghimire et al., 2019;
Prasad et al., 2017)) have already adopted maximum
overlap discrete wavelet transform for water resources,
solar radiation and energy prediction studies.
In the recent decade, the ensemble machine learning (EML) models, like stacking, bagging, and boosting methods, have been used dramatically in hydrologic
modeling; and simulating sediment transport in water
bodies has followed the same trend (Oehler et al., 2012;
Sharafati et al., 2020). Literature shows that the prevalent approaches in generating ensemble models aim to
enhance the general capability and accuracy of individual AI models in simulating/predicting/forecasting suspended sediment. This claim can be supported, based
on the results of: (i) stacking EML models (Alizadeh
et al., 2017; Shamaei & Kaedi, 2016), (ii) model averaging (Wang et al., 2015), (iii) bagging EMLs (Nhu et al.,
2020), (iv) boosting EMLs (Shadkani et al., 2020) and
(v) ensemble empirical model decomposition with adaptive noise algorithm for multi-scale river flow prediction
(Wen et al., 2019).
There is no universal AI model that can capture the
global watershed features, mainly because each model
presents its own limitations based on the algorithm and
watershed input features. This is evidenced by the nature
of various patterns due to the ‘regionalization effect of
the model accuracy in terms of hydrological alterations.’
Meanwhile, the results of AI models have proven the
sophisticated nature of these predictive tools or the predictive algorithms when implemented in various types
of watershed areas with diverse features, while modeling
and applying the non-linear hydrological parameters.
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
Optimization in engineering, specifically in hydrological and water management engineering, has been a
challenge for many decades. Additionally, optimization
methods play a vital role in the training of AI models.
As an illustration, standard types of AI using mathematical techniques, such as traditional gradient-based
optimization methods, were used to solve problems of
interest. Some studies reported the successful practice
of certain types of gradient-based optimization methods
like the Levenberg-Marquardt algorithm in hydrological modeling (Qanza et al., 2019; Zounemat-Kermani,
2012). Nonetheless, due to their intrinsic shortcomings
in addressing complexities in non-linear, chaotic, and
stochastic hydrological systems, effective and reliable
nature-inspired optimization techniques, called metaheuristic algorithms, have been under development. Due
to their robustness and efficiency in coping with the
complexities of noisy environment, they are superior to
traditional mathematical algorithms for exploring the
problem search space (Gomes et al., 2018). Recent literature shows that several attempts have been made to
enhance the efficiency of meta-heuristic optimizers in
training ordinary AI models (Kisi & Yaseen, 2019).
Comparing different versions of AI models, it is clear
that the integrated meta-heuristic AI models are the most
appropriate for utilization as a potential alternative to
the existing models for modeling river suspended sediment over diverse hydrological regions. Integrated metaheuristic AI models represent a hybridization between
nature-inspired optimization algorithms and classical AI
models, such as ANFIS, SVR, ANN, etc (Sharafati et al.,
2021). In this regard, Fadaee et al. (2020) assessed the
capability of using integrative (hybrid) AI models in
improving the suspended sediment predictions based
on two types of meta-heuristic algorithms, namely the
butterfly optimization algorithm (BOA) and the genetic
algorithm (GA). In general, the meta-heuristic algorithm
increased the accuracy of AI models (ANFIS and ANN)
by almost eight per cent. In such studies, however, limited
data were used, and therefore, their generalization was
also limited. More comprehensive studies related to the
implementation of AI techniques with nature-inspired
algorithms, considering that various stations have distinct geographical characteristics or climate, are needed.
Different optimization algorithms have been found to
work better with different AI algorithms for suspended
sediment modeling. Rahgoshay et al. (2019) showed that
the SVR model performed better in modeling the sediment transport amount when it was hybridized with PSO
than with the GA method. However, Darabi et al. (2021)
showed that ANFIS performed better when hybridized
with sine-cosine algorithm (SCA) than certain natureinspired optimization algorithms, such as PSO. There are
1601
no guidelines on which optimizer should be hybridized
with which AI algorithm for better prediction of sediment load. Different AI algorithms were individually
optimized in previous studies using different optimizers
to find the best AI-optimizer combination. However, only
a few AI and optimization algorithms have been evaluated. Darabi et al. (2021) examined the performance
of three AI models, namely ANN, ANFIS and RBRNN
models, with four optimization algorithms, namely sinecosine algorithm (SCA), PSO, firefly algorithm, and bat
algorithm, for sediment load prediction. Experiments
can be conducted using more AI and optimization algorithms for comparative performance evaluation of different AI-optimizer combinations.
It is evident from the literature that linear regression and sediment rating curves are generally used and
compared with AI methods for modeling SSL, and these
methods produce inferior results compared to the latter
methods. The MLR and SRC methods cannot adequately
map the hysteresis behavior of the Q-SSL relationship.
Therefore, it seems that comparison of highly non-linear
AI methods with MLR and/or SRC in modeling SSL is
not reasonable.
The other important issue in modeling SSL is that in
most studies, previous SSL values were used as model
inputs, which is hard to apply in practice because of the
difficulty in measuring SSL data especially in the case
of extreme events. On the other hand, the use of only
water level data instead of discharge carries much more
importance in modeling SSL, especially for the developing countries where effective variables are not available or
missing for most of the stations.
It is worth noting some of the limitations to the use
of AI approaches in modeling the SSL data series. It is
known that two variables, SSL and streamflow, are generally used for modeling SSL and these variables have
high spatial and temporal variability and mostly having highly skewed distributions. The main limitations of
the implemented AI models are their low applicability
to other basins which may have different morphological and climatic characteristics. The other limitation is
their black box nature and their physical interpretation
is a challenge and requires further analysis in quantifying
the relationship between the independent and dependent
parameters in a more reasonable way. Although the AI
methods have some advantages in local modeling of SSLs,
their limitations need to be addressed.
8. Insights for future research direction
Although several studies have endorsed ensemble learning as a more robust and effective artificial intelligence
1602
H. TAO ET AL.
paradigm, there still exists a significant gap in utilizing ensemble learning in constructing AI models for
suspended sediment concentration (Baskin et al., 2017;
Wang et al., 2011). In this sense, further research is
encouraged to explore the capability of different types
of ensemble learning, particularly novel methodologies
such as, AdaBoost and bagging techniques. Another topic
that should be pursued in the area of suspended sediment
modeling is the importance of bedload transportation
in river systems and its influence on the variations of
SSL. Although some studies consider both bedload and
SSL modeling using AI methods (Pektaş & Doğan, 2015;
Zounemat-Kermani, Mahdavi-Meymand, et al., 2020),
the vast majority of the published studies have focused
solely on the SSL as the target variable. Since employing AI models facilitates the process of encountering and
embedding different input parameters, it is suggested to
examine the effect of adding the bedload variable to the
input vector in improving the final accuracy.
There is a need to channel more effort towards extending the current scope of research in this area. Considering
these outcomes and the extensive state-of-the-art, the following recommendations can be considered for future
studies:
This review provides interesting findings on AI models, especially in the area of their practical applicability.
For instance, ELM, advanced version of ANN model
has shown fast-computational learning capability, which
has been qualified as an online expert predictive system
with great real-time application potential. It has been recommended for the monitoring of sediment transport,
which contributes to the operation of reservoir systems
and development of dependable irrigation systems and
water pollution control. Another benefit of SSL prediction is its importance to river engineering sustainability
from hydrological, environmental, and ecological perspectives. The general architecture of ELM is shown in
Figure 11.
In the single hidden layer ELM model, the random initialization of weight of internal network throughout the
learning process can determine its efficiency. However,
this framework can be an issue from the soft computing
viewpoint, since the learning performance of the network can be affected by the single hidden layer which
can lead to inaccurate predictions. Therefore, the extension of ELM to a deep learning NN model is needed,
where recurrent hidden layers are hosted in the feature space, thereby ensuring a better determination of
the internal weight. The presence of deep-learning-based
AI models to solve hydrological related problems is yet
to be explored; however, their high accuracy and fast
training/testing times have rendered them desirable for
modeling sediment transport.
Future applications of ELM as a hydrological prediction tool in areas with prominent hydrological feature
memories could hinge on the use of advanced versions of
the deep learning (DL) process which utilizes gradientbased Long Short-term Memory Network (LSTM) along
with recurrent (or convolutional) layers (Hochreiter &
Schmidhuber, 1997) as a predictive tool. The LSTMbased recurrent ELM can be applied in a way that LSTM
can learn to bridge the marginal time lags in the case
of discrete steps in order to enforce a constant error
flow through error carousels, thereby permitting multiplicative gate units to learn the open/close access to the
constant error flow. Such a model can implement input
data time-series lagged behavior, and the result can be
promising (Hochreiter & Schmidhuber, 1997; Sak et al.,
2014; Tian & Pan, 2015). The local features in space and
time can also be searched using LSTM-based ELM, since
it has a generally little computational complexity, which
can facilitate the extensive extraction of features with a
low latency of the hydrological related variable output.
Another possible future direction is the assessment of
prediction uncertainty, which has become a requirement
for most modeling within the hydrology and water quality studies (Bayram et al., 2013; Wan Mohtar et al., 2019).
Using the Lower Upper Bound Estimation method, Chen
and Chau (2019) attempted to address uncertainty analysis on a novel hybrid double feedforward neural network model for producing the sediment load prediction
interval with promising results. This provides a preparatory work and other uncertainty methods can be further
explored.
Beven (2016) suggested that the development of
hydrological science was being threatened by scarce
resources. Hence, the use of cost-effective soft computing
models should be considered rather than embarking on
costly experimental investigations. Therefore, this advantage must be exploited to build expert systems, which can
help solve real-world problems.
Suspended sediment flow is a complex non-linear
process that AI algorithms try to map by establishing
a relationship between inputs and output. It is often
suggested that AI models’ inputs should be selected
based on the non-linear relationship between inputs and
output, rather than using conventional linear statistical
approaches like correlation and auto-regression. In recent
years, RFE method has been integrated with AI algorithms like SVM and ANN to use the RFE-SVM and RFEANN to select input features based on non-linear inputsoutput relationships. For example, Pour et al. (2020) used
RFE-SVM to select inputs for a rainfall prediction model
of peninsular Malaysia using AI. Such non-linear feature selection methods can be employed to improve AI
models’ sediment prediction accuracy.
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
1603
Figure 11. Extreme learning machine general architecture.
The relationship between suspended sediment and its
driving factors may change with time. This is more likely
due to changes in rainfall and other drivers of river sedimentation in the contexts of climate and other environmental changes. Therefore, the predictive model developed using historical data becomes obsolete within a
short period. In recent years, the forward rolling method
has been used to make the AI prediction model adaptable
to changes in the input-output relationship. For example,
Khan et al. (2021) used a forward rolling AI algorithm
to develop a climate change resilient heatwave prediction model for Pakistan. Such approach can be used in
modeling suspended sediment to make them robust to
environmental changes.
Sediment data is unavailable in most of the rivers, even
in developed countries. Transformation of basin information from one gauged catchment to another nearby
ungauged catchment is often suggested to model the
ungauged catchment’s hydrological process. Recently, AI
models have been used to predict streamflow of one
catchment using rainfall and streamflow data of nearby
gauged catchment. For example, Kim and Song (2020)
trained a convolution neural network with data of a
gauged catchment and used it for streamflow prediction
of a nearby catchment. Kratzert et al. (2019) conducted
a review on predictions in ungauged basins using AI and
its potential. A similar study can be conducted to predict
sediment flow in ungauged or poorly gauged catchments
using nearby catchment data.
Big data is considered as the future solution to complex
prediction problem. An extensive amount of data from
different sources, including satellite, dense observational
network and governmental statistics, are used. AI models
can be used to integrate satellite high-resolution precipitation, land use, soil, and digital elevation model data
for real-time prediction of river sedimentation’s spatial
distribution. Such models have more potential to provide the required information for river sedimentation
management.
Obviously, the efficiency of AI models must not only
be based on its numerical accuracy, rather, but its practical implementation for water resources management
must be considered, especially, knowledge-based expert
systems. The physically-based modeling approaches, for
instance, have been proven useful and as excellent hydrological methodologies. Therefore, hybridization of the AI
modeling technique with the current decision-making
outline is still a difficult task, especially when considering the complicated nature of the system physics and
availability of information.
The impact of human activity on modeling has been
noted as a significant factor missing in the extensive
1604
H. TAO ET AL.
Figure 12. Proposed scheme of hybrid predictive models based on the hybridization of nature inspired optimization algorithms with
standard artificial intelligence models.
review. It is certain that human activities can significantly
affect the behavior of a basin of a multi-criteria catchment; therefore, such a constructive factor must not be
neglected as it can influence model performance.
Hybrid AI models have succeeded in handling nonstationary, random, and complex data (Tahani et al.,
2019). Consequently, these efforts should be encouraged
for the proposition of more coupled models in forthcoming research possibilities. Various nature-inspired (NI)
optimizers have been established over the years among
which many are still unmapped in the field of river suspended sediment modeling. Such optimizers are bacterial foraging optimization (Das et al., 2009), amoeba
based algorithm (Zhang et al., 2013), artificial plant
optimization (Cui et al., 2012), flower pollination algorithms (Yang, 2012), grasshopper-insect based algorithm
(Saremi et al., 2017), wasp-insect based algorithm (Theraulaz, 1991), fruitfly-insect based algorithm (Xing & Gao,
2014), glow- worm-insect based algorithm (Krishnanand
& Ghose, 2009), dragonfly-insect based algorithm (Mirjalili, 2016), shark optimization (Hersovici et al., 1998),
whale optimization (Mirjalili & Lewis, 2016), bean optimization (Zhang et al., 2010), doves-bird based algorithms (Su et al., 2009), eagle-bird based algorithms
(Yang & Deb, 2010), cuckoo search (Yang & Deb,
2009), bird mating (Askarzadeh & Rezazadeh, 2012),
monkey-animal based algorithms (Mucherino & Seref,
2007), wolf-animal based algorithms (Liu et al., 2011),
lion-animal based algorithms (Yazdani & Jolai, 2016),
and artificial fish-swarm algorithm (Li, 2003). A possible design of NI algorithm coupled with AI models applied to river sediment modeling is shown in
Figure 12.
Based on an experimental study (Juez et al., 2018)
concluded that the dynamics of fine sediment jointly
observed with discharge found in nature was different in
different reaches of the same river system. The morphological evolution of the riverbed within a reach (degradation or aggradation) is controlled by the importance
of the sediment available at that location relative to the
incoming sediment. The type of hysteretical loop modulated by the interaction of both the distal and proximal
supply is intrinsically related with these two types of
morphological processes of the riverbed. Consequently,
sediment processes as sediment hysteresis are difficult
to model adequately. This shows that adequately modeling of SSL needs new modeling strategies considering
the inputs including the morphological changes in the
riverbed (Juez et al., 2018).
Disclosure statement
No potential conflict of interest was reported by the author(s).
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
ORCID
Mohammad Zounemat-Kermani http://orcid.org/0000-0002
-1421-8671
Ozgur Kisi http://orcid.org/0000-0001-7847-5872
Kwok-Wing Chau http://orcid.org/0000-0001-6457-161X
Assefa M. Melesse http://orcid.org/0000-0003-4724-9367
Mohamed Elhakeem http://orcid.org/0000-0002-5753-7306
Shamsuddin Shahid http://orcid.org/0000-0001-9621-6452
References
Ab Ghani, A., & Azamathulla, H. M. (2014). Development
of GEP-based functional relationship for sediment transport in tropical rivers. Neural Computing and Applications.
https://doi.org/10.1007/s00521-012-1222-9
Adams, M. P., Ghisalberti, M., Lowe, R. J., Callaghan, D. P.,
Baird, M. E., Infantes, E., & O’Brien, K. R. (2018). Water residence time controls the feedback between seagrass, sediment
and light: Implications for restoration. Advances in Water
Resources, 117, 14–26. https://doi.org/10.1016/j.advwatres.
2018.04.004
Adib, A., & Mahmoodi, A. (2017). Prediction of suspended sediment load using ANN GA conjunction model with Markov
chain approach at flood conditions. KSCE Journal of Civil
Engineering, 21(1), 447–457. https://doi.org/10.1007/s12205
-016-0444-2
Afan, H. A., El-shafie, A., Mohtar, W. H. M. W., & Yaseen, Z. M.
(2016). Past, present and prospect of an Artificial Intelligence
(AI) based model for sediment transport prediction. Journal
of Hydrology, https://doi.org/10.1016/j.jhydrol.2016.07.048
Afan, H. A., Keshtegar, B., Mohtar, W. H. M. W., & ElShafie, A. (2017). Harmonize input selection for sediment
transport prediction. Journal of Hydrology, 552, 366–375.
https://doi.org/10.1016/j.jhydrol.2017.07.008
Ahmed, A. A., Deo, R. C., Raj, N., Ghahramani, A., Feng, Q.,
Yin, Z., & Yang, L. (2021a). Deep learning forecasts of soil
moisture: Convolutional neural network and gated recurrent
unit models coupled with satellite-derived MODIS, observations and synoptic-scale climate index data. Remote Sensing,
13(4), 554. https://doi.org/10.3390/rs13040554
Ahmed, A. A. M., Deo, R. C., Ghahramani, A., Raj, N., Feng,
Q., Yin, Z., & Yang, L. (2021b). LSTM integrated with
Boruta-random forest optimiser for soil moisture estimation under RCP4. 5 and RCP8. 5 Global warming scenarios. Stochastic Environmental Research and Risk Assessment,
1–31. https://doi.org/10.1007/s00477-021-01969-3
Akay, A. E., Erdas, O., Reis, M., & Yuksel, A. (2008). Estimating sediment yield from a forest road network by using
a sediment prediction model and GIS techniques. Building
and Environment, 43(5), 687–695. https://doi.org/10.1016/
j.buildenv.2007.01.047
Al-Musaylh, M. S., Deo, R. C., & Li, Y. (2020). Electrical
energy demand forecasting model development and evaluation with maximum overlap discrete wavelet transformonline sequential extreme learning machines algorithms.
Energies, 13(9), 2307. https://doi.org/10.3390/en13092307
Alizadeh, M. J., Jafari Nodoushan, E., Kalarestaghi, N., &
Chau, K. W. (2017). Toward multi-day-ahead forecasting
of suspended sediment concentration using ensemble models. Environmental Science and Pollution Research, 24(36),
28017–28025. https://doi.org/10.1007/s11356-017-0405-4
1605
Alp, M., & Cigizoglu, H. K. (2007). Suspended sediment
load simulation by two artificial neural network methods
using hydrometeorological data. Environmental Modelling
& Software, 22(1), 2–13. https://doi.org/10.1016/j.envsoft.
2005.09.009
Andrews, D. F. (1974). A robust method for multiple linear
regression. Technometrics, 16(4), 523–531. https://doi.org/
10.1080/00401706.1974.10489233
Armanini, A., Cavedon, V., & Righetti, M. (2015). A probabilistic/deterministic approach for the prediction of the sediment transport rate. Advances in Water Resources, 81, 10–18.
https://doi.org/10.1016/j.advwatres.2014.09.008
Askarzadeh, A., & Rezazadeh, A. (2012). A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: Bird mating optimizer. International Journal of Energy Research, 37(10), 1196–1204. https://doi.org/
10.1002/er.2915
Azamathulla, H. M., Cuan, Y. C., Ghani, A. A., & Chang, C.
K. (2012). Suspended sediment load prediction of river systems: GEP approach. Arabian Journal of Geosciences, 6(9),
3469–3480. https://doi.org/10.1007/s12517-012-0608-4
Baskin, I. I., Marcou, G., Horvath, D., & Varnek, A. (2017). Bagging and boosting of regression models. Tutorials in chemoinformatics. John Wiley & Sons. https://doi.org/10.1002/
9781119161110.ch16
Bayram, A., Kankal, M., Tayfur, G., & Önsoy, H. (2013). Prediction of suspended sediment concentration from water
quality variables. Neural Computing and Applications, 24(5),
1079–1087. https://doi.org/10.1007/s00521-012-1333-3
Betrie, G. D., Mohamed, Y. A., Griensven, A., & Srinivasan,
R. (2011). Sediment management modelling in the Blue
Nile Basin using SWAT model. Hydrology and Earth System
Sciences, 15(3), 807–818. https://doi.org/10.5194/hess-15
-807-2011
Beven, K. (2016). Advice to a young hydrologist. Hydrological Processes, 30(20), 3578–3582. https://doi.org/10.1002/
hyp.10879
Bhagat, S. K., Paramasivan, M., Al-Mukhtar, M., Tiyasha, T.,
Pyrgaki, K., Tung, T. M., & Yaseen, Z. M. (2021). Prediction of lead (Pb) adsorption on attapulgite clay using the
feasibility of data intelligence models. Environmental Science
and Pollution Research. https://doi.org/10.1007/s11356-021
-12836-7
Bhagat, S. K., Pyrgaki, K., Salih, S. Q., Tiyasha, T., Beyaztas,
U., Shahid, S., & Yaseen, Z. M. (2021). Prediction of copper ions adsorption by attapulgite adsorbent using tunedartificial intelligence model. Chemosphere, 276, 130162.
https://doi.org/10.1016/j.chemosphere.2021.130162
Bhagat, S. K., & Tiyasha, T. (2013). Impact of millions of tones
of effluent of textile industries: Analysis of textile industries effluents in Bhilwara and an approach with bioremediation. International Journal of ChemTech Research, 5(3),
1289–1298.
Bhagat, S. K., Tiyasha, T., Awadh, S. M., Tung, T. M., Jawad,
A. H., & Yaseen, Z. M. (2020). Prediction of sediment
heavy metal at the Australian Bays using newly developed
hybrid artificial intelligence models. Environmental Pollution, 115663. https://doi.org/10.1016/j.envpol.2020.115663
Bhagat, S. K., Tiyasha, T., & Bekele, D. N. (2018). Economical approaches for the treatment and reutilization of Laundry wastewater – A review. Journal of Industrial Pollution
Control, 34(2), 2164–2178.
1606
H. TAO ET AL.
Bhagat, S. K., Tiyasha, T., Tung, T. M., Mostafa, R. R., &
Yaseen, Z. M. (2020). Manganese (Mn) removal prediction
using extreme gradient model. Ecotoxicology and Environmental Safety, 204(August), 111059. https://doi.org/10.1016/
j.ecoenv.2020.111059
Bhagat, S. K., Tung, T. M., & Yaseen, Z. M. (2020). Heavy metal
contamination prediction using ensemble model: Case study
of Bay sedimentation, Australia. Journal of Hazardous Materials, 403, 123492. https://doi.org/10.1016/j.jhazmat.2020.
123492
Bharti, B., Pandey, A., Tripathi, S. K., & Kumar, D. (2017).
Modelling of runoff and sediment yield using ANN, LSSVR, REPTree and M5 models. Hydrology Research, 48(6),
1489–1507. https://doi.org/10.2166/nh.2017.153
Bisoyi, N., Gupta, H., Padhy, N. P., & Chakrapani, G. J. (2019).
Prediction of daily sediment discharge using a back propagation neural network training algorithm: A case study of
the Narmada River, India. International Journal of Sediment
Research, 34(2), 125–135. https://doi.org/10.1016/j.ijsrc.
2018.10.010
Breiman, L. (2001). Random forests. Machine Learning, 45(1),
5–32. https://doi.org/10.1023/A:1010933404324
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984).
Classification and regression trees. Wadsworth.
Buyukyildiz, M., & Kumcu, S. Y. (2017). An estimation of
the suspended sediment load using adaptive network based
fuzzy inference system, support vector machine and artificial
neural network models. Water Resources Management, 31(4),
1343–1359. https://doi.org/10.1007/s11269-017-1581-1
Chen, S., & Cowan, F. N. (1991). Orthogonal least squares
learning algorithm for radial basis function networks.
IEEE Transactions on Neural Networks, 2(2), 302–309.
https://doi.org/10.1109/72.80341
Chen, X., & Chau, K. (2019). Uncertainty analysis on hybrid
double feedforward neural network model for sediment load
estimation with LUBE method.
Chen, X. Y., & Chau, K. W. (2016). A hybrid double feedforward neural network for suspended sediment load estimation. Water Resources Management, 30(7), 2179–2194.
https://doi.org/10.1007/s11269-016-1281-2
Choubin, B., Darabi, H., Rahmati, O., Sajedi-Hosseini, F., &
Kløve, B. (2018). River suspended sediment modelling using
the CART model: A comparative study of machine learning
techniques. Science of the Total Environment, 615, 272–281.
https://doi.org/10.1016/j.scitotenv.2017.09.293
Cicek, Z. I. E., & Ozturk, Z. K. (2021). Optimizing the artificial neural network parameters using a biased random
key genetic algorithm for time series forecasting. Applied
Soft Computing, 102, 107091. https://doi.org/10.1016/j.asoc.
2021.107091
Cigizoglu, H. K. (2004). Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Advances
in Water Resources, 27(2), 185–195. https://doi.org/10.1016/
j.advwatres.2003.10.003
Cigizoglu, H. K., & Alp, M. (2006). Generalized regression neural network in modelling river sediment yield. Advances in
Engineering Software, 37(2), 63–68. https://doi.org/10.1016/
j.advengsoft.2005.05.002
Cortes, C., & Vapnik, V. (1995). Support vector machine.
Machine Learning, 20(3), 273–297.
Crichton, N. J., Hinde, J. P., & Marchini, J. (1997). Models for diagnosing chest pain: Is CART helpful? Statistics
in Medicine, 16(7), 717–727. https://doi.org/10.1002/(SICI)
1097-0258(19970415)16:7<717::AID-SIM504 > 3.0.
CO;2-E
Cui, Z., Yang, C., & Sanyal, S. (2012). Training artificial neural networks using APPM. International Journal of Wireless and Mobile Computing, 5(2), 168–174.
https://doi.org/10.1504/IJWMC.2012.046787
Darabi, H., Mohamadi, S., Karimidastenaei, Z., Kisi, O.,
Ehteram, M., ELShafie, A., & Haghighi, A. T. (2021). Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models. Soft Computing, 1–18. https://doi.org/10.1007/s00500-021-05721-5
Das, S., Dasgupta, S., Biswas, A., Abraham, A., & Konar,
A. (2009). On stability of the chemotactic dynamics in
bacterial-foraging optimization algorithm. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and
Humans, 39(3), 670–679. https://doi.org/10.1109/TSMCA.
2008.2011474
Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communications on Pure and Applied
Mathematics, 41(7), 909–996. https://doi.org/10.1002/cpa.
3160410705
De Vente, J., Poesen, J., & Verstraeten, G. (2005). The application of semi-quantitative methods and reservoir sedimentation rates for the prediction of basin sediment yield in Spain.
Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.
2004.08.030
Demirci, M., & Baltaci, A. (2013). Prediction of suspended sediment in river using fuzzy logic and multilinear regression
approaches. Neural Computing and Applications, 23(Suppl.
1), 145–151. https://doi.org/10.1007/s00521-012-1280-z
Dietterich, T. G. (2000). Ensemble methods in machine learning. In Oncogene (Vol. 12, pp. 1–15). https://doi.org/10.1007/
3-540-45014-9_1.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system:
Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, https://doi.org/10.1109/3477.484436
Du, K.-L., & Swamy, M. N. S. (2019). Recurrent neural
networks. In Neural networks and statistical learning (pp.
351–371). Springer.
Duy Vinh, V., Ouillon, S., Van Thao, N., & Ngoc Tien,
N. (2016). Numerical simulations of suspended sediment
dynamics due to seasonal forcing in the Mekong coastal area.
Water, 8(6), 255. https://doi.org/10.3390/w8060255
Emamgholizadeh, S., & Demneh, R. K. (2019). A comparison of
artificial intelligence models for the estimation of daily suspended sediment load: A case study on the Telar and Kasilian
rivers in Iran. Water Science and Technology: Water Supply,
19(1), 165–178. https://doi.org/10.2166/ws.2018.062
Evaristo, J., & McDonnell, J. J. (2019). Global analysis
of streamflow response to forest management. Nature.
https://doi.org/10.1038/s41586-019-1306-0
Fadaee, M., Mahdavi-Meymand, A., & Zounemat-Kermani, M.
(2020). Suspended sediment prediction using integrative soft
computing models: On the analogy between the butterfly
optimization and genetic algorithms. Geocarto International.
1–14. https://doi.org/10.1080/10106049.2020.1753821
Fang, H.-W., & Wang, G.-Q. (2000). Three-dimensional mathematical model of suspended-sediment transport. Journal
of Hydraulic Engineering, 126(8), 578–592. https://doi.org/
10.1061/(asce)0733-9429(2000)126:8(578)
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
Ferreira, C. (2001). Gene expression programming: A new
adaptive algorithm for solving problems. Complex Systems,
13(2), 87–129. arXiv preprint cs/0102027.
Francke, T., López-Tarazón, J. A., & Schröder, B. (2008). Estimation of suspended sediment concentration and yield
using linear models, random forests and quantile regression forests. Hydrological Processes: An International Journal,
22(25), 4892–4904. https://doi.org/10.1002/hyp.7110
Friedman, J. H. (1991). Multivariate adaptive regression splines.
The Annals of Statistics, 19(1), 1–67. https://doi.org/10.1214/
aos/1176347963
Ghimire, S., Deo, R. C., Raj, N., & Mi, J. (2019). Wavelet-based
3-phase hybrid SVR model trained with satellite-derived
predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction.
Renewable and Sustainable Energy Reviews, 113, 109247.
https://doi.org/10.1016/j.rser.2019.109247
Gholami, V., Booij, M. J., Nikzad Tehrani, E., & Hadian, M. A.
(2018). Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data. Catena, 163(April),
210–218. https://doi.org/10.1016/j.catena.2017.12.027
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms
and machine learning. Machine Learning, 3(2), 95–99.
https://doi.org/10.1023/A:1022602019183
Gomes, W. J. S., Beck, A. T., Lopez, R. H., & Miguel, L. F. F.
(2018). A probabilistic metric for comparing metaheuristic optimization algorithms. Structural Safety, 70, 59–70.
https://doi.org/10.1016/j.strusafe.2017.10.006
Gregory, P. (2005). Bayesian logical data analysis for the physical
sciences: A comparative approach with mathematica® support.
Cambridge University Press.
Greig, S. M., Sear, D. A., & Carling, P. A. (2005). The
impact of fine sediment accumulation on the survival
of incubating salmon progeny: Implications for sediment management. Science of the Total Environment.
https://doi.org/10.1016/j.scitotenv.2005.02.010
Grossmann Jean, A. (1984). Decomposition of hardy functions into square integrable wavelets of constant shape.
SIAM Journal on Mathematical Analysis, 15(4), 723–736.
https://doi.org/10.1137/0515056
Halecki, W., Kruk, E., & Ryczek, M. (2018). Estimations
of nitrate nitrogen, total phosphorus flux and suspended
sediment concentration (SSC) as indicators of surfaceerosion processes using an ANN (Artificial Neural Network) based on geomorphological parameters in mountainous catchments. Ecological Indicators, 91(March), 461–469.
https://doi.org/10.1016/j.ecolind.2018.03.072
Hamaamin, Y. A., Nejadhashemi, A. P., Zhang, Z., Giri, S.,
Adhikari, U., & Herman, M. R. (2018). Evaluation of neurofuzzy and Bayesian techniques in estimating suspended
sediment loads. Sustainable Water Resources Management.
https://doi.org/10.1007/s40899-018-0225-9
Harrell, F. E. (2015). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and
survival analysis. Springer.
Hassanpour, F., Sharifazari, S., Ahmadaali, K., Mohammadi,
S., & Sheikhalipour, Z. (2019). Development of the FCMSVR hybrid model for estimating the suspended sediment
load. KSCE Journal of Civil Engineering, 23(6), 2514–2523.
https://doi.org/10.1007/s12205-019-1693-7
Heathcote, I. W. (2009). Integrated watershed management:
Principles and practice. John Wiley & Sons.
1607
Heddam, S., & Kisi, O. (2018). Modelling daily dissolved
oxygen concentration using least square support vector
machine, multivariate adaptive regression splines and M5
model tree. Journal of Hydrology. https://doi.org/10.1016/
j.jhydrol.2018.02.061
Hersovici, M., Jacovi, M., Maarek, Y. S., Pelleg, D., Shtalhaim,
M., & Ur, S. (1998). The shark-search algorithm. An application: Tailored web site mapping. Computer Networks and
ISDN Systems, 30(1–7), 317–326. https://doi.org/10.1016/
S0169-7552(98)00038-5
Himanshu, S. K., Asce, S. M., Pandey, A., & Yadav, B. (2016).
Ensemble wavelet-support vector machine approach for prediction of suspended sediment load using hydrometeorological data. Journal of Hydrologic Engineering, 22(3), 1–14.
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001516
Himanshu, S. K., Pandey, A., & Yadav, B. (2017). Assessing the applicability of TMPA-3B42V7 precipitation dataset
in wavelet-support vector machine approach for suspended sediment load prediction. Journal of Hydrology, 550,
103–117. https://doi.org/10.1016/j.jhydrol.2017.04.051
Hochreiter, S., & Schmidhuber, J. J. (1997). Long short-term
memory. Neural Computation, 9(8), 1–32. https://doi.org/
10.1162/neco.1997.9.8.1735
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013).
Applied logistic regression. John Wiley & Sons.
Huang, L., Fang, H., & Reible, D. (2015). Mathematical model
for interactions and transport of phosphorus and sediment
in the Three Gorges Reservoir. Water Research, 85, 393–403.
https://doi.org/10.1016/j.watres.2015.08.049
Jha, S. K., & Bombardelli, F. A. (2011). Theoretical/numerical
model for the transport of non-uniform suspended sediment in open channels. Advances in Water Resources, 34(5),
577–591. https://doi.org/10.1016/j.advwatres.2011.02.001
Juez, C., Hassan, M. A., & Franca, M. J. (2018). The origin of fine
sediment determines the observations of suspended sediment fluxes under unsteady flow conditions. Water Resources
Research, 54(8), 5654–5669. https://doi.org/10.1029/
2018WR022982
Karaboga, D., & Basturk, B. (2007). A powerful and efficient
algorithm for numerical function optimization: Artificial
bee colony (ABC) algorithm. Journal of Global Optimization,
39(3), 459–471. https://doi.org/10.1007/s10898-007-9149-x
Kaveh, K., Duc Bui, M., & Rutschmann, P. (2017). A comparative study of three different learning algorithms applied
to ANFIS for predicting daily suspended sediment concentration. International Journal of Sediment Research, 32(3),
340–350. https://doi.org/10.1016/j.ijsrc.2017.03.007
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization, 1942–1948.
Khan, M. Y. A., Tian, F., Hasan, F., & Chakrapani, G. J.
(2019). Artificial neural network simulation for prediction
of suspended sediment concentration in the River Ramganga, Ganges Basin, India. International Journal of Sediment Research, 34(2), 95–107. https://doi.org/10.1016/j.ijsrc.
2018.09.001
Khan, N., Shahid, S., Ismail, T. B., & Behlil, F. (2021). Prediction of heat waves over Pakistan using support vector machine algorithm in the context of climate change.
Stochastic Environmental Research and Risk Assessment,
1–19. https://doi.org/10.1007/s00477-020-01963-1
Khosravi, K., Mao, L., Kisi, O., Yaseen, Z. M., & Shahid,
S. (2018). Quantifying hourly suspended sediment load
1608
H. TAO ET AL.
using data mining models: Case study of a glacierized
Andean catchment in Chile. Journal of Hydrology, 567(June),
165–179. https://doi.org/10.1016/j.jhydrol.2018.10.015
Kim, D. Y., & Song, C. M. (2020). Developing a discharge
estimation model for ungauged watershed using CNN and
hydrological image. Water, 12(12), 3534. https://doi.org/
10.3390/w12123534
Kişi, Ö. (2009). Evolutionary fuzzy models for river suspended
sediment concentration estimation. Journal of Hydrology.
https://doi.org/10.1016/j.jhydrol.2009.03.036
Kisi, O., Choubin, B., Deo, R. C., & Yaseen, Z. M. (2019). Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models. Hydrological Sciences Journal. https://doi.org/
10.1080/02626667.2019.1632460
Kisi, O., Dailr, A. H., Cimen, M., & Shiri, J. (2012). Suspended sediment modeling using genetic programming and
soft computing techniques. Journal of Hydrology, 450–451,
48–58. https://doi.org/10.1016/j.jhydrol.2012.05.031
Kisi, O., & Ozkan, C. (2016). A new approach for modeling sediment-discharge relationship: Local weighted linear regression. Water Resources Management, 31(1), 1–23.
https://doi.org/10.1007/s11269-016-1481-9
Kisi, O., & Yaseen, Z. M. (2019). The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment
concentration prediction. Catena, 174(May 2018), 11–23.
https://doi.org/10.1016/j.catena.2018.10.047
Kisi, O., & Zounemat-Kermani, M. (2016). Suspended sediment modeling using neuro-fuzzy embedded fuzzy c-means
clustering technique. Water Resources Management, 30(11),
3979–3994. https://doi.org/10.1007/s11269-016-1405-8
Klecka, W. R., Iversen, G. R., & Klecka, W. R. (1980). Discriminant analysis (Vol. 19). Sage.
Koza, J. R. (1994). Genetic programming as a means for programming computers by natural selection. Statistics and
Computing,
4(2),
87–112.
https://doi.org/10.1007/
BF00175355
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S. (2019). Toward improved predictions in ungauged basins: Exploiting the power of machine
learning. Water Resources Research, 55(12), 11344–11354.
https://doi.org/10.1029/2019WR026065
Krishnanand, K. N., & Ghose, D. (2009). Glowworm swarm
optimization for simultaneous capture of multiple local
optima of multimodal functions. Swarm Intelligence, 3(2),
87–124. https://doi.org/10.1007/s11721-008-0021-5
Kumar, A., Kumar, P., & Singh, V. K. (2019). Evaluating different machine learning models for runoff and suspended
sediment simulation. Water Resources Management, 33(3),
1217–1231. https://doi.org/10.1007/s11269-018-2178-z
Leisenring, M., & Moradkhani, H. (2012). Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation. Journal of Hydrology, 468, 268–282.
https://doi.org/10.1016/j.jhydrol.2012.08.049
Lek, S., & Guégan, J. F. (1999). Artificial neural networks as
a tool in ecological modelling, an introduction. Ecological
Modelling, 120(2–3), 65–73. https://doi.org/10.1016/S0304
-3800(99)00092-7
Lenzi, M. A., & Marchi, L. (2000). Suspended sediment load
during floods in a small stream of the Dolomites (northeastern Italy). CATENA, 39(4), 267–282. https://doi.org/10.1016/
s0341-8162(00)00079-5
Lewis, R. J., Ph, D., & Street, W. C. (2000). An introduction to classification and regression tree (CART) analysis.
2000 Annual Meeting of the Society for Academic Emergency
Medicine, (310), 14 p. https://doi.org/10.1.1.95.4103.
Li, X. L. (2003). A new intelligent optimization-artificial fish
swarm algorithm. Doctor Thesis, Zhejiang University of Zhejiang.
Liu, C., Yan, X., Liu, C., & Wu, H. (2011). The wolf colony
algorithm and its application. Chinese Journal of Electronics,
20(2), 212–216.
Liu, J., Zhou, Z., & Zhang, X. J. (2019). Impacts of sediment
load and size on rill detachment under low flow discharges.
Journal of Hydrology, 570, 719–725. https://doi.org/10.1016/
j.jhydrol.2019.01.033
Liu, Q. J., Zhang, H. Y., Gao, K. T., Xu, B., Wu, J. Z., &
Fang, N. F. (2019). Time-frequency analysis and simulation of the watershed suspended sediment concentration
based on the Hilbert-Huang transform (HHT) and artificial neural network (ANN) methods: A case study in the
Loess Plateau of China. Catena, 179(April 2018), 107–118.
https://doi.org/10.1016/j.catena.2019.03.042
Maier, H. R., & Dandy, G. C. (1999). Empirical comparison
of various methods for training feed-forward neural networks for salinity forecasting. Water Resources Research,
35(8), 2591–2596. https://doi.org/10.1029/1999WR900150
Malagó, A., Bouraoui, F., Vigiak, O., Grizzetti, B., & Pastori, M.
(2017). Modelling water and nutrient fluxes in the Danube
River Basin with SWAT. Science of the Total Environment.
https://doi.org/10.1016/j.scitotenv.2017.05.242
Malik, A., Kumar, A., Kisi, O., & Shiri, J. (2019). Evaluating
the performance of four different heuristic approaches with
Gamma test for daily suspended sediment concentration
modeling. Environmental Science and Pollution Research.
https://doi.org/10.1007/s11356-019-05553-9
Mamdani, E. H., & Assilian, S. (1975). An experiment in
linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.
https://doi.org/10.1016/S0020-7373(75)80002-2
Merkhali, S. P., Ehteshami, M., & Sadrnejad, S. A. (2015).
Assessment quality of a nonuniform suspended sediment transport model under unsteady flow condition
(case study: Aras River). Water and Environment Journal.
https://doi.org/10.1111/wej.12137
Meshram, S. G., Ghorbani, M. A., Deo, R. C., Kashani, M. H.,
Meshram, C., & Karimi, V. (2019). New approach for sediment yield forecasting with a two-phase feedforward neuron
network-particle swarm optimization model integrated with
the gravitational search algorithm. Water Resources Management, 33(7), 2335–2356. https://doi.org/10.1007/s11269019-02265-0
Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic
optimization technique for solving single-objective, discrete,
and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073. https://doi.org/10.1007/s00521
-015-1920-1
Mirjalili, S., & Lewis, A. (2016). The whale optimization
algorithm. Advances in Engineering Software, 95, 51–67.
https://doi.org/10.1016/j.advengsoft.2016.01.008
Moeeni, H., & Bonakdari, H. (2018). Impact of normalization and input on ARMAX-ANN model performance
in suspended sediment load prediction. Water Resources
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
Management, 32(3), 845–863. https://doi.org/10.1007/s11
269-017-1842-z
Mount, N., & Stott, T. (2008). A discrete Bayesian network to
investigate suspended sediment concentrations in an Alpine
proglacial zone. Hydrological Processes, 22(18), 3772–3784.
https://doi.org/10.1002/hyp.6981
Mucherino, A., & Seref, O. (2007). Monkey search: a novel
metaheuristic search for global optimization. In AIP conference proceedings (Vol. 953, pp. 162–173). AIP.
Nguyen, K. D., Guillou, S., Chauchat, J., & Barbry, N. (2009).
A two-phase numerical model for suspended-sediment
transport in estuaries. Advances in Water Resources, 32(8),
1187–1196. https://doi.org/10.1016/j.advwatres.2009.04.001
Nhu, V.-H., Rahmati, O., Falah, F., Shojaei, S., Al-Ansari, N.,
Shahabi, H., & Ahmad, B. (2020). Mapping of groundwater
spring potential in karst aquifer system using novel ensemble bivariate and multivariate models. Water, 12(4), 985.
https://doi.org/10.3390/w12040985
Noori, R., Karbassi, A. R., Moghaddamnia, A., Han, D.,
Zokaei-Ashtiani, M. H., Farokhnia, A., & Gousheh, M.
G. (2011). Assessment of input variables determination
on the SVM model performance using PCA, Gamma
test, and forward selection techniques for monthly stream
flow prediction. Journal of Hydrology, 401(3–4), 177–189.
https://doi.org/10.1016/j.jhydrol.2011.02.021
Nourani, V., Hosseini Baghanam, A., Adamowski, J., & Kisi, O.
(2014). Applications of hybrid wavelet–artificial intelligence
models in hydrology: A review. Journal of Hydrology, 514,
358–377. https://doi.org/10.1016/j.jhydrol.2014.03.057
Nourani, V., Molajou, A., Tajbakhsh, A. D., & Najafi, H. (2019).
A wavelet based data mining technique for suspended sediment load modeling. Water Resources Management, 33(5),
1769–1784. https://doi.org/10.1007/s11269-019-02216-9
Oehler, F., Coco, G., Green, M. O., & Bryan, K. R. (2012). A
data-driven approach to predict suspended-sediment reference concentration under non-breaking waves. Continental Shelf Research, 46, 96–106. https://doi.org/10.1016/j.csr.
2011.01.015
Olyaie, E., Banejad, H., Chau, K.-W., & Melesse, A. M.
(2015). A comparison of various artificial intelligence
approaches performance for estimating suspended sediment load of river systems: A case study in United States.
Environmental Monitoring and Assessment, 187(4), 189.
https://doi.org/10.1007/s10661-015-4381-1
Pearl, J. (2014). Probabilistic reasoning in intelligent systems:
Networks of plausible inference. Elsevier.
Pektas, A. O., & Cigizoglu, H. K. (2017a). Investigating the
extrapolation performance of neural network models in suspended sediment data. Hydrological Sciences Journal, 62(10),
1694–1703. https://doi.org/10.1080/02626667.2017.1349316
Pektas, A. O., & Cigizoglu, H. K. (2017b). Long-range forecasting of suspended sediment. Hydrological Sciences Journal,
62(14), 2415–2425. https://doi.org/10.1080/02626667.2017.
1383607
Pektaş, A. O., & Doğan, E. (2015). Prediction of bed
load via suspended sediment load using soft computing
methods. Geofizika, 32(1), 27–46. https://doi.org/10.15233/
gfz.2015.32.2
Percival, D., & Walden, B. (2000). Wavelet methods for time
series analysis. Cambridge Univ. Press.
Pour, S. H., Wahab, A. K. A., & Shahid, S. (2020). Physicalempirical models for prediction of seasonal rainfall extremes
1609
of Peninsular Malaysia. Atmospheric Research, 233, 104720.
https://doi.org/10.1016/j.atmosres.2019.104720
Prasad, R., Deo, R. C., Li, Y., & Maraseni, T. (2017). Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin
region using IIS and MODWT algorithm. Atmospheric
Research, 197, 42–63. https://doi.org/10.1016/j.atmosres.
2017.06.014
Qanza, H., Maslouhi, A., Hachimi, M., & Hmimou, A.
(2019). Simultaneous estimation of hydro-dipersive parameters using a new modified levenberg-marquardt algorithm.
MATEC Web of Conferences, 286, 6001. https://doi.org/
10.1051/matecconf/201928606001
Qin, W., Wang, L., Lin, A., Zhang, M., & Bilal, M. (2018).
Improving the estimation of daily aerosol optical depth and
aerosol radiative effect using an optimized artificial neural
network. Remote Sensing. https://doi.org/10.3390/rs10071
022
Qin, W., Wang, L., Lin, A., Zhang, M., Xia, X., Hu, B., &
Niu, Z. (2018). Comparison of deterministic and data-driven
models for solar radiation estimation in China. Renewable
and Sustainable Energy Reviews, 81(May 2017), 579–594.
https://doi.org/10.1016/j.rser.2017.08.037
Qin, W., Wang, L., Zhang, M., Niu, Z., Luo, M., Lin, A., & Hu,
B. (2019). First effort at constructing a high-density photosynthetically active radiation dataset during 1961–2014 in
China. Journal of Climate. https://doi.org/10.1175/JCLI-D
-18-0590.1
Quilty, J., & Adamowski, J. (2018). Addressing the incorrect
usage of wavelet-based hydrological and water resources
forecasting models for real-world applications with best
practices and a new forecasting framework. Journal of
Hydrology, https://doi.org/10.1016/j.jhydrol.2018.05.003
Quinlan, J. R. (1992). Learning with continuous classes. In 5th
Australian joint conference on artificial intelligence (Vol. 92,
pp. 343–348). Singapore.
Raghavendra, S., & Deka, P. C. (2014). Support vector machine
applications in the field of hydrology: A review. Applied Soft
Computing Journal, 19, 372–386. https://doi.org/10.1016/
j.asoc.2014.02.002
Rahgoshay, M., Feiznia, S., Arian, M., Ali, S., & Hashemi,
A. (2018). Modeling daily suspended sediment load using
improved support vector machine model and genetic
algorithm.
Rahgoshay, M., Feiznia, S., Arian, M., & Hashemi, S. A. A.
(2019). Simulation of daily suspended sediment load using
an improved model of support vector machine and genetic
algorithms and particle swarm. Arabian Journal of Geosciences, 12(9), https://doi.org/10.1007/s12517-019-4444-7
Rajaee, T. (2011). Wavelet and ANN combination model for
prediction of daily suspended sediment load in rivers.
Science of the Total Environment, 409(15), 2917–2928.
https://doi.org/10.1016/j.scitotenv.2010.11.028
Rechenberg, I. (1994). Evolutionsstrategie. frommann-holzboog.
Riahi-Madvar, H., & Seifi, A. (2018). Uncertainty analysis
in bed load transport prediction of gravel Bed Rivers by
ANN and ANFIS. Arabian Journal of Geosciences, 11(21), 21.
https://doi.org/10.1007/s12517-018-3968-6
Rice, S., & Church, M. (1996). Sampling surficial fluvial
gravels; the precision of size distribution percentile sediments. Journal of Sedimentary Research, 66(3), 654–665.
https://doi.org/10.2110/jsr.66.654
1610
H. TAO ET AL.
Rosenblatt, F. (1961). Principles of neurodynamics. Perceptrons
and the theory of brain mechanisms. Cornell Aeronautical
Lab Inc Buffalo NY.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1988). Learning representations by back-propagating errors. Cognitive
Modeling, 5(3), 1.
Sadeghi, S. H., & Singh, V. P. (2017). Dynamics of suspended
sediment concentration, flow discharge and sediment particle size interdependency to identify sediment source. Journal of Hydrology, 554, 100–110. https://doi.org/10.1016/
j.jhydrol.2017.09.006
Sak, H., Senior, A., & Beaufays, F. (2014). Long shortterm memory recurrent neural network architectures for
large scale acoustic modeling. Interspeech, 2014(September),
338–342. https://doi.org/1402.1128
Sakizadeh, M. (2020). Spatial distribution and source identification together with environmental health risk assessment of PAHs along the coastal zones of the USA. Environmental Geochemistry and Health, 42(10), 3333–3350.
https://doi.org/10.1007/s10653-020-00578-3
Samarasinghe, S. (2006). Neural networks for applied Sciences
and engineering. Taylor & Francis Group, LLC.
Samet, K., Hoseini, K., Karami, H., & Mohammadi, M. (2019).
Comparison between soft computing methods for prediction of sediment load in rivers: Maku Dam case study. Iranian Journal of Science and Technology – Transactions of Civil
Engineering, 43(1), 93–103. https://doi.org/10.1007/s40996
-018-0121-4
Sang, Y.-F. (2013). A review on the applications of wavelet
transform in hydrology time series analysis. Atmospheric
Research, 122, 8–15. https://doi.org/10.1016/j.atmosres.
2012.11.003
Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: Theory and application. Advances in
Engineering Software, 105, 30–47. https://doi.org/10.1016/
j.advengsoft.2017.01.004
Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P.
L. (2000). New support vector algorithms. Neural Computation, 12(5), 1207–1245. https://doi.org/10.1162/089976600
300015565
Senthil Kumar, A. R., Ojha, C. S. P., Goyal, M. K., Singh, R. D.,
& Swamee, P. K. (2012). Modeling of suspended sediment
concentration at Kasol in India using ANN, fuzzy logic, and
decision tree algorithms. Journal of Hydrologic Engineering,
17(3), 394–404. https://doi.org/10.1061/(ASCE)HE.1943
-5584.0000445
Shadkani, S., Abbaspour, A., Samadianfard, S., Hashemi, S.,
Mosavi, A., & Band, S. S. (2020). Comparative study of multilayer perceptron-stochastic gradient descent and gradient
boosted trees for predicting daily suspended sediment load:
The case study of the Mississippi River, US. International
Journal of Sediment Research. https://doi.org/10.1016/j.ijsrc.
2020.10.001
Shamaei, E., & Kaedi, M. (2016). Suspended sediment concentration estimation by stacking the genetic programming and
neuro-fuzzy predictions. Applied Soft Computing Journal, 45,
187–196. https://doi.org/10.1016/j.asoc.2016.03.009
Sharafati, A., Haghbin, M., Motta, D., & Yaseen, Z. M. (2019).
The application of soft computing models and empirical formulations for hydraulic structure scouring depth simulation:
A comprehensive review, assessment and possible future
research direction. Archives of Computational Methods
in Engineering, 1–25. https://doi.org/10.1007/s11831-01909382-4
Sharafati, A., Haji Seyed Asadollah, S. B., Motta, D., & Yaseen,
Z. M. (2020). Application of newly developed ensemble
machine learning models for daily suspended sediment
load prediction and related uncertainty analysis. Hydrological Sciences Journal, https://doi.org/10.1080/02626667.2020.
1786571
Sharafati, A., Masoud, H., Tiwari, N. K., Bhagat, S. K.,
Al-Ansari, N., Chau, K.-W., & Yaseen, Z. M. (2021).
Performance evaluation of sediment ejector efficiency
using hybrid neuro-fuzzy models. Engineering Applications of Computational Fluid Mechanics, 15(1), 627–643.
https://doi.org/10.1080/19942060.2021.1893224
Sharghi, E., Nourani, V., Najafi, H., & Gokcekus, H. (2019).
Conjunction of a newly proposed emotional ANN (EANN)
and wavelet transform for suspended sediment load modeling. Water Supply, 1–9. https://doi.org/10.2166/ws.2019.044
Sharghi, E., Nourani, V., Najafi, H., & Soleimani, S. (2019).
Wavelet-exponential smoothing: A new hybrid method for
suspended sediment load modeling. Environmental Processes. 6(1), 191–218. https://doi.org/10.1007/s40710-019
-00363-0
Shojaeezadeh, S. A., Nikoo, M. R., McNamara, J. P., AghaKouchak, A., & Sadegh, M. (2018). Stochastic modeling of suspended sediment load in alluvial rivers. Advances in Water
https://doi.org/10.1016/j.advwatres.2018.06.
Resources.
006
Simons, D. B., & Şentürk, F. (1992). Sediment transport technology: Water and sediment dynamics. Water Resources Publication.
Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality – A case study. Ecological Modelling, 220(6), 888–895.
https://doi.org/10.1016/j.ecolmodel.2009.01.004
Sinha, R., Gupta, A., Mishra, K., Tripathi, S., Nepal, S.,
Wahid, S. M., & Swarnkar, S. (2019). Basin-scale hydrology and sediment dynamics of the Kosi river in the
Himalayan foreland. Journal of Hydrology, 570, 156–166.
https://doi.org/10.1016/j.jhydrol.2018.12.051
Singh, V. K., Kumar, D., Kashyap, P. S., & Kisi, O. (2018). Simulation of suspended sediment based on gamma test, heuristic, and regression-based techniques. Environmental Earth
Sciences, 77(19). https://doi.org/10.1007/s12665-018-7892-6
Su, M. C., Su, S. Y., & Zhao, Y. X. (2009). A swarm-inspired projection algorithm. Pattern Recognition, 42(11), 2764–2786.
https://doi.org/10.1016/j.patcog.2009.03.020
Sudhishri, S., Kumar, A., & Singh, J. K. (2016). Comparative
evaluation of neural network and regression based models
to simulate runoff and sediment yield in an outer Himalayan
Watershed. Journal of Agricultural Science and Technology,
18(3), 681–694.
Suif, Z., Fleifle, A., Yoshimura, C., & Saavedra, O. (2016).
Spatio-temporal patterns of soil erosion and suspended sediment dynamics in the Mekong River Basin. Science of the
Total Environment, 568, 933–945. https://doi.org/10.1016/
j.scitotenv.2015.12.134
Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support
vector machine classifiers. Neural Processing Letters, 9(3),
293–300. https://doi.org/10.1023/A:1018628609742
Tahani, M., Yousefi, H., Noorollahi, Y., & Fahimi, R. (2019).
Application of nature inspired optimization algorithms in
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
optimum positioning of pump-as-turbines in water distribution networks. Neural Computing and Applications.
https://doi.org/10.1007/s00521-018-3566-2
Takagi, H., & Hayashi, I. (1988). Artificial-neural-networkdriven fuzzy reasoning. In Workshop on fuzzy system application (pp. 217–218).
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems
and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics, 15(1), 116–132.
https://doi.org/10.1109/TSMC.1985.6313399
Tao, H., Keshtegar, B., & Yaseen, Z. M. (2019). The feasibility
of integrative radial basis M5Tree predictive model for river
suspended sediment load simulation. Water Resources Management, 33(13), 4471–4490. https://doi.org/10.1007/s11269
-019-02378-6
Theraulaz, G. (1991). Task differentiation in Polistes wasp
colonies: a model for self-organizing groups of robots. In
Proceedings of the First International Conference on Simulation of Adaptive Behavior: From Animals to Animates, 1991
(pp. 346–355). The MIT Press.
Tian, Y., & Pan, L. (2015). Predicting short-term traffic
flow by long short-term memory recurrent neural network. In 2015 IEEE International Conference on Smart
City/SocialCom/SustainCom (SmartCity) (pp. 153–158).
https://doi.org/10.1109/SmartCity.2015.63.
Tiyasha, T., & Yaseen, Z. M. (2020). A survey on river
water quality modelling using artificial intelligence models:
2000–2020. Journal of Hydrology. https://doi.org/10.1016/
j.jhydrol.2020.124670
Ulke, A., Tayfur, G., & Ozkul, S. (2017). Investigating a suitable empirical model and performing regional analysis for
the suspended sediment load prediction in major rivers of
the Aegean region, Turkey. Water Resources Management.
https://doi.org/10.1007/s11269-016-1357-z
Verstraeten, G., Poesen, J., de Vente, J., & Koninckx, X. (2003).
Sediment yield variability in Spain: A quantitative and semiqualitative analysis using reservoir sedimentation rates. Geomorphology, 50(4), 327–348. https://doi.org/10.1016/S0169
-555X(02)00220-9
Walling, D. E. (1977). Assessing the accuracy of suspended
sediment rating curves for a small basin. Water Resources
https://doi.org/10.1029/
Research,
13(3),
531–538.
wr013i003p00531
Walling, D. E., & Collins, A. L. (2008). The catchment sediment budget as a management tool. Environmental Science
& Policy, 11(2), 136–143. https://doi.org/10.1016/j.envsci.
2007.10.004
Wan Mohtar, W. H. M., Abdul Maulud, K. N., Muhammad, N. S., Sharil, S., & Yaseen, Z. M. (2019). Spatial
and temporal risk quotient based river assessment for
water resources management. Environmental Pollution, 248,
133–144. https://doi.org/10.1016/j.envpol.2019.02.011
Wang, G., Hao, J., Ma, J., & Jiang, H. (2011). A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications, 38(1), 223–230.
https://doi.org/10.1016/j.eswa.2010.06.048
Wang, L., Kisi, O., Zounemat-Kermani, M., & Li, H. (2017).
Pan evaporation modeling using six different heuristic computing methods in different climates of China. Journal of
Hydrology, 544, 407–427. https://doi.org/10.1016/j.jhydrol.
2016.11.059
1611
Wang, L., Kisi, O., Zounemat-Kermani, M., Zhu, Z., Gong,
W., Niu, Z., & Liu, Z. (2017). Prediction of solar radiation in China using different adaptive neuro-fuzzy methods
and M5 model tree. International Journal of Climatology.
https://doi.org/10.1002/joc.4762
Wang, S., Flanagan, D. C., & Engel, B. A. (2019). Estimating sediment transport capacity for overland flow. Journal
of Hydrology, 123985. https://doi.org/10.1016/j.jhydrol.2019.
123985
Wang, X., Yen, H., Jeong, J., & Williams, J. R. (2015). Accounting for conceptual soil erosion and sediment yield modeling
uncertainty in the APEX model using Bayesian model averaging. Journal of Hydrologic Engineering, 20(6), C4014010.
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001
119
Wen, X., Feng, Q., Deo, R. C., Wu, M., Yin, Z., Yang, L., & Singh,
V. P. (2019). Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff
prediction problems. Journal of Hydrology, 570, 167–184.
https://doi.org/10.1016/j.jhydrol.2018.12.060
Williams, J. R., & Berndt, H. D. (1976). Sediment yield prediction based on watershed hydrology. American Society of
Agricultural Engineering.
Wu, L., Yao, W., & Ma, X. (2018). Using the comprehensive governance degree to calibrate a piecewise sediment
delivery ratio algorithm for dynamic sediment predictions: A case study in an ecological restoration watershed
of northwest China. Journal of Hydrology, 564, 888–899.
https://doi.org/10.1016/j.jhydrol.2018.07.072
Wu, W., Dandy, G. C., & Maier, H. R. (2014). Protocol for developing ANN models and its application to the assessment
of the quality of the ANN model development process in
drinking water quality modelling. Environmental Modelling
and Software, 54, 108–127. http://www.scopus.com/inward/
record.url?eid=2-s2.0-84892886293&partnerID=40&md5
https://doi.org/
=d674e77b09b399ef7aeca582f4ab94f6.
10.1016/j.envsoft.2013.12.016
Xing, B., & Gao, W.-J. (2014). Fruit fly optimization algorithm.
In Innovative computational intelligence: A rough guide to
134 clever algorithms (pp. 167–170). Springer.
Yadav, A., Chatterjee, S., & Equeenuddin, S. M. (2018). Suspended sediment yield estimation using genetic algorithmbased artificial intelligence models: Case study of Mahanadi
River, India. Hydrological Sciences Journal, 63(8), 1162–1182.
https://doi.org/10.1080/02626667.2018.1483581
Yang, X.-S., & Deb, S. (2009). Cuckoo search via Lévy flights.
In 2009 World Congress on Nature & Biologically Inspired
Computing (NaBIC) (pp. 210–214). IEEE.
Yang, X.-S., & Deb, S. (2010). Eagle strategy using Lévy walk
and firefly algorithms for stochastic optimization. In Nature
inspired cooperative strategies for optimization (NICSO 2010)
(pp. 101–111). Springer.
Yang, X. S. (2012, September). Flower pollination algorithm for
global optimization. In International conference on unconventional computing and natural computation (pp. 240–249).
Springer.
Yaseen, Z. M., Ebtehaj, I., Kim, S., Sanikhani, H., Asadi,
H., Ghareb, M. I., & Al-Ansari, N. (2019). Novel hybrid
data-intelligence model for forecasting monthly rainfall
with uncertainty analysis. Water, 11(3), 502. https://doi.org/
10.3390/w11030502
1612
H. TAO ET AL.
Yaseen, Z. M., Mohtar, W. H. M. W., Ameen, A. M. S.,
Ebtehaj, I., Razali, S. F. M., Bonakdari, H., & Shahid, S.
(2019). Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: Case
study in tropical region. IEEE Access, 7, 74471–74481.
https://doi.org/10.1109/ACCESS.2019.2920916
Yaseen, Z. M., Zigale, T. T., Kumar, R., Salih, S. Q.,
Awasthi, S., Tung, T. M., & Bhagat, S. K. (2019). Laundry
wastewater treatment using a combination of sand filter,
bio-char and teff straw media. Scientific Reports, 9(1), 1–11.
https://doi.org/10.1038/s41598-019-54888-3
Yazdani, M., & Jolai, F. (2016). Lion optimization algorithm
(LOA): a nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering, 3(1), 24–36.
https://doi.org/10.1016/j.jcde.2015.06.003
Yilmaz, B., Aras, E., Nacar, S., & Kankal, M. (2018). Estimating
suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Science of the Total Environment, 639,
826–840. https://doi.org/10.1016/j.scitotenv.2018.05.153
Younger, M. S. (1979). Handbook for linear regression (Vol. 1).
Duxbury Press North Scituate.
Zhang, C., & Ma, Y. (2012). Ensemble machine learning: Methods and applications. Springer.
Zhang, X., Sun, B., Mei, T., & Wang, R. (2010). Post-disaster
restoration based on fuzzy preference relation and bean optimization algorithm. In 2010 IEEE Youth Conference on Information, Computing and Telecommunications (pp. 271–274).
IEEE.
Zhang, X., Zhang, Y., Hu, Y., Deng, Y., & Mahadevan, S. (2013).
An adaptive amoeba algorithm for constrained shortest
paths. Expert Systems with Applications, 40(18), 7607–7616.
https://doi.org/10.1016/j.eswa.2013.07.054
Zhao, H., Yang, S., Yang, B., & Huang, Y. (2017). Quantifying anthropogenic and climatic impacts on sediment
load in the sediment-rich region of the Chinese Loess
Plateau by coupling a hydrological model and ANN.
StochasticEnvironmental Research and Risk Assessment, 31(8),
2057–2073. https://doi.org/10.1007/s00477-017-1381-4
Zhou, W., Zhang, L., & Jiao, L. (2002). Linear programming support vector machines. Pattern Recognition, 35(12),
https://doi.org/10.1016/S0031-3203(01)00
2927–2936.
210-2
Zounemat-Kermani, M. (2012). Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorology
and Atmospheric Physics, 117(3–4), 181–192. https://doi.org/
10.1007/s00703-012-0192-x
Zounemat-Kermani, M. (2017). Assessment of several nonlinear methods in forecasting suspended sediment concentration in streams. Hydrology Research, 48(5), 1240–1252.
https://doi.org/10.2166/nh.2016.219
Zounemat-Kermani, M., Beheshti, A.-A., Ataie-Ashtiani, B.,
& Sabbagh-Yazdi, S.-R. (2009). Estimation of currentinduced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Applied
Soft Computing, 9(2), 746–755. https://doi.org/10.1016/
j.asoc.2008.09.006
Zounemat-Kermani, M., Kişi, Ö, Adamowski, J., & RamezaniCharmahineh, A. (2016). Evaluation of data driven models for river suspended sediment concentration modeling.
Journal of Hydrology, 535, 457–472. https://doi.org/10.1016/
j.jhydrol.2016.02.012
Zounemat-Kermani, M., Mahdavi-Meymand, A., Alizamir,
M., Adarsh, S., & MundherYaseen, Z. (2020). On the
complexities of sediment load modeling using integrative machine learning: An application to the great river
of loíza in Puerto Rico. Journal of Hydrology, 124759.
https://doi.org/10.1016/j.jhydrol.2020.124759
Zounemat-Kermani, M., Matta, E., Cominola, A., Xia, X.,
Zhang, Q., Liang, Q., & Hinkelmann, R. (2020). Neurocomputing in surface water hydrology and hydraulics: A review
of two decades retrospective, current status and future
prospects. Journal of Hydrology. https://doi.org/10.1016/
j.jhydrol.2020.125085