Hydroinformatics

In recent decades, due to reduction in precipitation, groundwater resource management has become one of the most important issues considered to prevent loss of water. Many solutions are concerned with the investigation of groundwater flow behavior. In this regard, development of meshless methods for solving the groundwater flow system equations in both complex and simple aquifers' geometry make them useful tools for such investigations. The independency of these methods to meshing and remeshing, as well as its capability in both reducing the computation requirement and presenting accurate results, make them receive more attention than other numerical methods. In this study, meshless local Petrov–Galerkin (MLPG) is used to simulate groundwater flow in Birjand unconfined aquifer located in Iran in a transient state for 1 year with a monthly time step. Moving least squares and cubic spline are employed as approximation and weight functions respectively and the simulated head from M...

Communities are at risk from extreme events and natural disasters that can lead to dangerous situations for residents. Improving resilience by helping people learn how to better prepare for, recover from, and adapt to disasters is critical to reduce the impacts of these extreme events. This project presents an intelligent system, Flood AI, designed to improve societal preparedness for flooding by providing a knowledge engine that uses voice recognition, artificial intelligence, and natural language processing based on a generalized ontology for disasters with a primary focus on flooding. The knowledge engine uses flood ontology to connect user input to relevant knowledge discovery channels on flooding by developing a data acquisition and processing framework using environmental observations, forecast models, and knowledge bases. The framework's communication channels include web-based systems, agent-based chatbots, smartphone applications, automated web workflows, and smart home devices, opening the knowledge discovery for flooding to many unique use cases.

In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum...

Flooding in urban areas represents a particular challenge to modellers and flood risk managers because of the complex interactions of surface and sewer flows. Quantified flood risk estimates provide a common metric that can be used to compare risks from different sources. In situations where there are several organisations responsible for flood risk management we wish to be able to disaggregate the total risk and attribute it to different components in the system and/or agents with responsibility for risk reduction in ...

Abstract
Details are given of the increasing global public concern relating to hydro‐environmental issues and examples are cited of some of the water quality and contaminant problems now being considered by river managers on a regular basis. The limitations and restrictions of both physical and numerical hydro‐environmental models are discussed and concerns raised about the increasing use being made by non‐specialist river engineers and scientists of complex hydroinformatics tools – often with a minimal appreciation of the complex hydrodynamic, bio‐ and geo‐chemical, sediment transport or computational processes involved. General details are given of numerical models used for flow, water quality, sediment transport and heavy metal concentration predictions in river basin systems and particular emphasis is focused on the influence of some master variables on two key hydro‐environmental processes. These processes include the decay of coliform bacteria and the partitioning of heavy metals. Two case studies are then discussed. In the first of these studies the velocity field and faecal coliform levels were predicted for a hypothetical discharge into a freshwater lake, namely Cardiff Bay, U.K., where the effects of dynamic decay rates were found to be significant. In the second study velocity, suspended sediment and heavy metal concentration distributions were predicted along the Mersey Basin, U.K., with dynamic partitioning coefficients being found to give improved agreement between predicted and measured heavy metal fluxes.

In this study, data mining using box plots and multivariate statistical analysis using factor analysis are employed for a spatio-temporal analysis of coastal water quality data from Tolo Harbour, Hong Kong. The analysis of box plots reveals pronounced spatial heterogeneity of the parameters studied. The spatial analysis clearly shows monitoring station TM2 in the Harbour Subzone to be most susceptible to eutrophication with the highest nutrient and algal biomass concentrations. The factor analysis brings to light dominant parameters to the ecological system under the coastal marine environment. The temporal analysis confirms the considerable decline in nutrient levels in recent years. In spite of this decline, the factor analysis indicates that nutrient processes play an important role even in recent years, suggesting an adequate supply of nutrients. It seems that they are being released from sources other than known point sources, possibly from nutrients accumulated in the sediment...

Este documento describe de manera sencilla las mayores cualidades de ICATEST V1.0., una herramienta computacional facilita el cálculo de gran variedad y cantidad de índices de calidad del agua e índices  contaminación, los cuales se presentan discriminados por país y/o autor. ICATEST V1.0 permite generar y guardar, tanto reportes como historiales, además de realizar estudios comparativos de calidad del agua, y es el software de mayor utilización en Latinoamérica por investigadores y gestores ambientales.

Rainfall-runoff simulation and prediction in watersheds is one of the most important tasks in water resources management. In this research, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vector machine (SVM). In addition, the particle swarm optimization (PSO) is used to determine free parameters of SVM. The study data from a large size catchment of the Yellow River in China are used to illustrate the performance of the proposed model. In order to measure the forecasting capability of the model, an ordinary least-squares (OLS) regression and a typical three-layer feed-forward artificial neural network (ANN) are employed as the benchmark model. The performance of the models was tested using the root mean squared error (RMSE), the average absolute relative error (AARE), the coefficient of correlation (R) and Nash–Sutcliffe efficiency (NSE). The PSO–SVM–EEMD model improved ANN model forecasting (65.99%) and  OLS regression (64.40%), and reduced RMSE (67.7%) and AARE (65.38%) values. Improvements of the forecasting results regarding the R and NSE are 8.43%, 18.89% and 182.7%, 164.2%, respectively. Consequently, the presented methodology in this research can enhance significantly rainfall-runoff forecasting at the studied station.

La valoración de la calidad del agua puede ser entendida como la evaluaciópn de su naturaleza química, física y biológica. en relación con la calidad natural, los efectos humanos y usos posibles. Con el objeto de hacer más simple la interpretación de los datos de su monitoreo, se han generado tanto los indices de calidad (ICAs), como los índices de contaminación (ICOs), los cuales reducen una gran cantidad de parámetros a una expresión simple de fácil interpretación entre técnicos, administradores ambientales y el público en general. La principal diferencia entre unos y otros esta en la forma en como ellos evaluan los procesos de contaminación y el número de variables tenidas en cuenta en la formulación respectiva para cada índice.

En este contexto, Fernández y Solano, ofrecen una visión panorámica de los aspectos principales de la valoración de la calidad del agua a través de indicadores de calidad y contaminación, a la vez que presentan en el mismo sentido, un análisis comparativo de los ICOs e ICAs de importancia mundial, sobre la base de su estructura matemática, similitud de parámetros y comportamiento interno frente a grupos de datos iguales.

Estre trabajo llena un vacio existente en latinoamerica y sienta las bases para un mejor entendimiento. análisis y desarrollo de los sistemas de indicación en los ámbitos académicos, investigativos y de gestión integrada de los recursos hídricos.

Se destaca ademas, el prologo del Biologo Marino y Ph.D. Alberto Ramírez, autor de innumerables obras en el área de la ecología y profesor de la Universidad Javeriana.

Hydroinformatics concerns applications of advanced information technologies in the fields of hydro-sciences and engineering. The rapid advancement and indeed the very success of hydroinformatics is directly associated with these applications. The aim of this paper is to provide an overview of some recent advances and to illustrate the practical implications of hydroinformatics technologies. A selection of characteristic examples on various topics is presented here, demonstrating the practical use at Delft Hydraulics. Most surely they will be elaborated upon in a more detailed way in forthcoming issues of this Journal. First, a very brief historical background is outlined to characterise the emergence and evolution of hydroinformatics in hydraulic and environmental engineering practice. Recent advances in computational hydraulics are discussed next. Numerical methods are outlined whose main advantages lie in their efficiency and applicability to a very wide range of practical problem...

Rainfall runoff is a very complicated process due to its nonlinear and multidimensional dynamics, and hence difficult to model. There are several options for a modeller to consider, for example: the type of input data to be used, the length of model calibration (training) data and whether or not the input data be treated as signals with different frequency bands so that they can be modelled separately. This paper
describes a new hybrid modelling scheme to answer the above mentioned questions. The proposed methodology
is based on a hybrid model integrating wavelet transformation, a modelling engine (Artificial
Neural Network) and the Gamma Test. First, the Gamma Test is used to decide the required input data dimensions and its length. Second, the wavelet transformation decomposes the input signals into different frequency bands. Finally, a modelling engine (ANN in this study) is used to model the decomposed signals separately. The proposed scheme was tested using the Brue catchment, Southwest England, as
a case study and has produced very positive results. The hybrid model outperforms all other models tested. This study has a wider implication in the hydrological modelling field since its general framework could be applied to other model combinations (e.g., model engine could be Support Vector Machines, neuro-fuzzy systems, or even a conceptual model. The signal decomposition could be carried out by Fourier
transformation).

Information on ongoing climate change impacts on water availability is limited for Asian regions, particularly for Peninsular Malaysia. Annual flash floods are common during peak monsoon seasons, while the dry seasons are hit by droughts, leading to socio-economic stress. This study, for the first time, analyzed the long-term trends (14 years, from 2002 to 2014) in terrestrial water storage and groundwater storage for Peninsular Malaysia, using Gravity Recovery And Climate Experiment data. Results indicate a decline in net terrestrial and groundwater storage over the last decade. Spatially, the northern regions are more affected by droughts, while the southern regions have more flash floods. Groundwater storage trends show strong correlations to the monsoon seasons, indicating that most of the shallow aquifer groundwater is used. Results also indicate that, with proper planning and management, excess monsoon/flash flood water can be stored in water storage structures up to the order...

Identification and management of the groundwater quality are of utmost importance for maintaining freshwater resources in arid and semi-arid areas, which is essential for sustainable development. Based on the quality of the groundwater in various areas, local policymakers and water resource managers can allocate the usage of resources for either drinking or agricultural purposes. This research aims to identify suitable areas of water pumping for drinking and agricultural harvest in the Tabriz aquifer, located in East Azerbaijan province, northwest Iran. A groundwater compatibility study was conducted by analyzing Electrical conductivity (EC), total dissolved solids (TDS), Chloride (Cl), Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Sulfate (SO4), Total hardness (TH), Bicarbonate (HCO3), pH, carbonate (CO3), the and Sodium Adsorption Ratio (SAR) obtained from 39 wells in the time period from 2003 to 2014. The Water Quality Index (WQI) and irrigation water quality (IWQ) index are respectively utilized due to their high importance in identifying the quality of water resources for irrigation and drinking purposes. The WQI index zoning for drinking classified water as excellent, good, or poor. The study concludes that most drinking water harvested for urban and rural areas is 'excellent water' or 'good water'. The IWQ index average for the study area is reported to be in the range of 25.9 to 34.55. The results further revealed that about 37 percent (296 km 2) of groundwater has high compatibility, and 63 percent of the study area (495 km 2) has average compatibility for agricultural purposes. The trend of IWQ and WQI indexes demonstrates that groundwater quality has been declining over time.

A mixed integer optimization model is formulated for capacity optimization of a hydropower storage project with control on reliability of meeting the project's firm energy yield. Particle swarm optimization (PSO) is used as the optimization algorithm, in which the method of sequential streamflow routing is called for objective function evaluations. Two types of problems are studied. The first one is an optimal design problem, in which the reservoir's normal and minimum operating levels as well as the powerplant's production capacity are optimized while reservoir releases are determined using a predefined operating policy. Two models are presented for this problem. In the first one (model A1), the normal and minimum operating levels are considered as the PSO decision variables, whereas the production capacity is iteratively adjusted in the simulation model. In the second model (model A2), the production capacity is also searched for by the PSO and the reliability constrai...

"An accurate forecast of river stage is very significant so that there is ample time for the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures as required. Since a variety of existing process-based hydrological models involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution. In this paper, a split-step particle swarm optimization (PSO) model is developed and applied to train multi-layer perceptrons for forecasting real-time water levels at Fo Tan in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging station (Tin Sum) or at Fo Tan. This paradigm is able to combine the advantages of global search capability of PSO algorithm in the first step and local fast convergence of Levenberg-Marquardt algorithm in the second step. The results demonstrate that it is able to attain a higher accuracy in a much shorter time when compared with the benchmarking backward propagation algorithm as well as the standard PSO algorithm.

Keywords: river stage; forecasting; split-step; particle swarm; optimization; Levenberg-Marquardt algorithm; artificial neural networks"

Genetic programming (GP) is a widely used machine learning (ML) algorithm that has been applied in water resources science and engineering since its conception in the early 1990s. However, similar to other ML applications, the GP algorithm is often used as a data fitting tool rather than as a model building instrument. We find this a gross underutilization of the GP capabilities. The most unique and distinct feature of GP that makes it distinctly different from the rest of ML techniques is its capability to produce explicit mathematical relationships between input and output variables. In the context of theory-guided data science (TGDS) which recently emerged as a new paradigm in ML with the main goal of blending the existing body of knowledge with ML techniques to induce physically sound models. Hence, TGDS has evolved into a popular data science paradigm, especially in scientific disciplines including water resources. Following these ideas, in our prior work, we developed two hydr...

Vertical slot fishways are hydraulic structures which allow the upstream migration of fish through obstructions in rivers. The appropriate design of these devices should take into account the behavior and biological requirements of the target fish species. However, little is known at the present time about fish behavior in these artificial conditions, which hinders the development of more effective fishway design criteria. In this work, an efficient technique to study fish trajectories and behavior in vertical slot fishways is proposed. It uses computer vision techniques to analyze images collected from a camera system and effectively track fish inside the fishway. Edge and region analysis algorithms are employed to detect fish in extreme image conditions and Kalman filtering is used to track fish along time. The proposed solution has been extensively validated through several experiments, obtaining promising results which may help to improve the design of fish passage devices.

This paper presents an acoustic leak detection system for distribution water mains using machine learning methods. The problem is formulated as a binary classifier to identify leak and no-leak cases using acoustic signals. A supervised learning methodology has been employed using several detection features extracted from acoustic signals, such as power spectral density and time-series data. The training and validation data sets have been collected over several months from multiple cities across North America. The proposed solution includes a multi-strategy ensemble learning (MEL) using a gradient boosting tree (GBT) classification model, which has performed better in maximizing detection rate and minimizing false positives as compared with other classification models such as KNN, ANN, and rule-based techniques. Further improvements have been achieved using a multitude of GBT classifiers combined in a parallel ensemble method called bagging algorithm. The proposed MEL approach demons...

This paper presents a new freeware simulation tool (IberWQ) for 2D water quality modelling in rivers and non-stratified estuaries. The model computes the spatial and temporal evolution of several species and variables which are relevant for the evaluation of the environmental status of rivers and estuaries, including: Escherichia coli, dissolved oxygen, carbonaceous biochemical oxygen demand, organic nitrogen, ammoniacal nitrogen, nitrate–nitrite nitrogen, water temperature and salinity. A depth-averaged transport equation is solved for each variable with a mass conservative unstructured finite volume solver. IberWQ is fully coupled to the hydrodynamic module of the software Iber, a freeware simulation tool for solving the 2D shallow water equations. Both models are integrated in the same windows graphical environment, including several tools which allow the user to generate unstructured meshes adapted to the site topography, define spatially variable input parameters and visualize ...

With the increase in frequency and severity of flash flood events in major cities around the world, the infrastructure and people living in those urban areas are exposed continuously to high risk levels of pluvial flooding. The situation is likely to be exacerbated by the potential impact of future climate change. A fast flood model could be very useful for flood risk analysis. One-dimensional (1D) models provide limited information about the flow dynamics whereas two-dimensional (2D) models require substantial computational time and cost, a factor that limits their use. This paper presents an alternative approach using cellular automata (CA) for 2D modelling. The model uses regular grid cells as a discrete space for the CA setup and applies generic rules to local neighbourhood cells to simulate the spatio-temporal evolution of pluvial flooding. The proposed CA model is applied to a hypothetical terrain and a real urban area. The synchronous state updating rule and inherent nature o...

"With increase in the number and total capacity of hydropower plants in power systems, optimality algorithms with a single objective are not suitable for optimizing the operation of complex hydropower systems to meet complex demands. Hydropower plants should prioritize discrepant objectives, such as peak regulation and maximizing generation during solving of optimal operation problems of hydropower systems. In this article, we present a multi-step progressive optimality algorithm (MSPOA) for the short-term hydroscheduling (STHS) problem to improve the quality of optimal solutions and enhance the convergence speed of progressive optimality algorithm (POA). In MSPOA, the original problem is first decomposed into a sequence of problems with the longer time steps. Next, the problem with the longest time step is solved, and the optimal solution is used as the initial solution for the problem with the second longest time step. This process proceeds until the original problem with the shortest time step is solved. The proposed discrepant-objective method and solution technique are tested for two types of hydroelectric systems. The results show that MSPOA can give better solutions and cost less time than POA due to enlarging feasible range of decision variables and reducing the number of computational stages. Discrepant objectives among hydropower plants can express the operation characteristics of complex hydropower systems more accurately than unique objective or multiple objectives.

Keywords: short-term hydroscheduling; discrepant objectives; progressive optimality algorithm; multi-step progressive optimality algorithm"

Large dams have provided extensive benefits during the past 60 years but have also resulted in ecological and social consequences that were unexpected or were deemed to have a lower societal importance than the design benefits. The management of large dams is still a relatively new scientific issue, compared to the timeframe necessary to detect and understand all the consequences occurring at the watershed scale. This paper summarizes the unforeseen or unanticipated environmental consequences of these projects and potential ramifications to the overall project performance. The value of a central knowledge base and the importance of a system-wide monitoring program to assess pre- and post-implementation conditions and adapt operational rules are presented. Knowledge developed in several basins is reviewed in the context of future strategies for Chile. Chile has a strong economy, looming energy crisis and is faced with balancing the long-term value of a renowned natural landscape with...

Boukan Dam reservoir is the largest infrastructure constructed on the Zarineh-Roud River regulating streamflow for different uses including supplying water to Lake Urmia, the second largest salt lake in the world. This paper presents a forecast-based adaptive real-time optimal operation model (ARTOM) for Boukan reservoir with the aim of maximizing releases feeding the lake while meeting other needs such as irrigation, industrial, and domestic uses. Adaptive neuro-fuzzy system-based inflow-to-reservoir forecasts are used in the ARTOM to determine optimal releases from the reservoir for future months up to the end of a year, but only the current period's release is applied. At the beginning of the next period, the forecasts are updated, and the procedure is repeated until the last period of the year. Additionally, the optimal terminal end-of-year reservoir storage volume is a dynamic updating input to the ARTOM, which is estimated from the results of a long-term reservoir operatio...