The frequency of extreme hydrological events varies highly in Taiwan, and increasing attention has been paid to the optimal reservoir operations. This study establishes an optimization model for watershed management through reservoir... more
The frequency of extreme hydrological events varies highly in Taiwan, and increasing attention has been paid to the optimal reservoir operations. This study establishes an optimization model for watershed management through reservoir operations subject to human and ecosystem needs. The Shihmen Reservoir in Taiwan is used as a case study. This study adopts the Taiwan Eco-hydrological Indicator System (TEIS) to classify river flow patterns. We combine the non-dominated sorting genetic algorithm II (NSGA-II) with the self-organizing radial basis network (SORBN) to develop the optimal model of reservoir operation. The results indicate that it is possible to simultaneously satisfy human and ecosystem needs, where ecosystem diversity can be retained in high SI values (1.7-1.9) while human demands can also be highly satisfied (α higher than 0.85). The proposed approach allows decision makers to easily determine the best compromise in water allocation through the trade-off between human and...
Taiwan is located in themonsoon zone of the North Pacific Ocean and experiences an average of 4-5 typhoons annually. The particular topography of Taiwan makes rivers short and steep, and thus rivers flow rapidly from catchments to... more
Taiwan is located in themonsoon zone of the North Pacific Ocean and experiences an average of 4-5 typhoons annually. The particular topography of Taiwan makes rivers short and steep, and thus rivers flow rapidly from catchments to reservoirs within a few hours during typhoon events. This study aims to construct realtime multi-step-ahead reservoir inflow forecast models by using Artificial Neural Networks (ANNs) based on radar rainfall data and reservoir inflow data. The Back PropagationNeural Network (BPNN) and the Recurrent Neural Network (RNN) are adopted for forecasting. Results indicate that the correlation coefficients in the testing phases of both models exceed 0.86 for one- to three-hour-ahead forecasts and exceed 0.69 for six-hour-head forecasts. The RNN model outperforms the BPNN model, which indicates the recurrent property of the RNN can effectively improve forecast accuracy when making several step-ahead forecasts. Results demonstrates that the constructed multi-step-ahe...
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Investigation on environmental flow for conservation of river ecosystem has been focused on ecological flow regime approach which is more comprehensive than the traditional minimum flow management schemes that merely consider single flow... more
Investigation on environmental flow for conservation of river ecosystem has been focused on ecological flow regime approach which is more comprehensive than the traditional minimum flow management schemes that merely consider single flow value. Therefore, the pivotal difficulty in developing ecological flow regime is how to take into account the interaction and relation between flow regime and river ecosystem. In this study we first present an idea of considering the relation between ecological flow regime and fish communities and then applying the gradient analysis technique in quantitative ecology theory to the ecological response model. The model is built by using the fish abundance (diversity) and the Taiwan Ecohydrology Indicator System (TEIS). Moreover, the introduction of dummy variables represent synthetic environment gradient to identify the niche in each fish species on ecohydrological gradient axis. The main advantages of this technique are: (1)Approximate the natural flo...
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This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling... more
This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN-static neural network; NARX network-dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly in...
Research Interests: Engineering, Environmental Science, Computer Science, Data Mining, Water quality, and 12 moreEnvironmental Monitoring, Rivers, Water Pollution, Medicine, Spatio Temporal Analysis, Multidisciplinary, Phosphorus, Nitrogen, Phosphates, Water Quality, Artificial Neural Network, and Computer Science Neural Networks
Groundwater over-exploitation has produced many critical problems in the southern Taiwan. The accumulated stresses and demands make groundwater management a complex issue that needs innovative scientific analyses for deriving better water... more
Groundwater over-exploitation has produced many critical problems in the southern Taiwan. The accumulated stresses and demands make groundwater management a complex issue that needs innovative scientific analyses for deriving better water management strategies. In this study, we aimed to provide scientific analyses of the groundwater systems in the Pingtung Plain through soft-computing techniques to explore its spatial-temporal and hydro-geological characteristics for the elaboration of future groundwater management plans and in decision-making process. We conducted a study to assess the essential features of the groundwater systems based on the long-term large datasets of regional groundwater levels by using the principal component analysis (PCA), and the self-organizing map (SOM) with regression analysis. The PCA results demonstrated that two leading components could well present the spatial characteristics of the groundwater systems and classify the region into eastern, western a...
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ABSTRACT In Taiwan, groundwater commonly becomes important water resources in dry periods, and/or areas lack of water storage facility due to its low cost, steady water supply and good water quality. However, improper groundwater... more
ABSTRACT In Taiwan, groundwater commonly becomes important water resources in dry periods, and/or areas lack of water storage facility due to its low cost, steady water supply and good water quality. However, improper groundwater development brings about serious decreases in groundwater levels and land subsidence which causes disasters, such as seawater intrusion or soil salination, accompanied with environmental and economic losses. It is critical to develop strategies for water resources conservation in mountainous areas. The complex heterogeneity of mountainous physiographic environment makes it challenging in the forecasts of groundwater level variations, particularly in mountainous areas. Artificial neural networks (ANNs) have been recognized as an effective modeling tool for complex nonlinear systems in the last two decades. This study aims to investigate the interactive mechanisms of groundwater at the mountainous areas of the Jhuoshuei river basin in central Taiwan through analyzing and modeling the groundwater level variations. Several issues are discussed in this study, which includes the correlation between groundwater level variation and rainfall as well as streamflow, the identification of groundwater recharge patterns and effective rainfall thresholds for estimating groundwater level variations. The results indicate: (1) the daily variation of groundwater level is closely correlated with river flow and one-day antecedent rainfall based on correlation analyses; (2) effective rainfall thresholds can be identified successfully; (3) groundwater level variations can be classified into four types for monitoring wells; and (4) the daily variations of groundwater level can be well estimated by constructed ANNs. The identified interactive mechanisms between surface water and groundwater can facilitate the mountainous water resource conservation strategy for better water management, especially irrigation water supply and for alleviating land subsidence in downstream areas in the future.
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Eevaporation is one of the most essential references to management of agricultural irrigation. In this study, a hybrid model for estimating evaporation at any ungauged site was developed by combining the artificial neural network and the... more
Eevaporation is one of the most essential references to management of agricultural irrigation. In this study, a hybrid model for estimating evaporation at any ungauged site was developed by combining the artificial neural network and the Kriging method. Data measured at nineteen meteorological gauging stations covering whole Taiwan in the period of 2007-2009 were collected, in which data of sixteen stations were used for model training and validation while data of the other three stations were adopted for testing the model's accuracy at ungauged sites. First of all, the Adaptive Network-based Fuzzy Inference System (ANFIS) model was established for the estimation of evaporation. Second, the error between observation and ANFIS output was used for calculating the residual at ungauged sites by using Kriging with spatial interpolation. Finally, the evaporation estimation at ungauged sites can be achieved by summing up the ANFIS output and residual obtained from Kriging. The results ...
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The pumping stations are the major hydraulic facilities for the elimination of flood in highly developed cities and therefore play an important role in flood mitigation in metropolitan area. Accurate predictions of inner water level in... more
The pumping stations are the major hydraulic facilities for the elimination of flood in highly developed cities and therefore play an important role in flood mitigation in metropolitan area. Accurate predictions of inner water level in urban drainage systems are necessary and important for successful operation of pumping stations. In view of the characteristics of artificial neural networks (ANNs), the model was introduced in this study for extracting rainfall-water level patterns from torrential rain events. The Yu-Cheng pumping station, Taipei city, is used as a case study, where historical records which contain information of rainfall amounts and inner water levels are used to train and verify the ANN's performance. First, we directly construct the ANN for multistep ahead water level predictions by using 11 storm events at gauging sites. The optimal structure and parameters are then tested via 3 different events. Second, the storm water management model (SWMM) was utilized fo...
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A reliable forecast of future events possesses great value. The main purpose of this paper is to propose an innovative learning technique for reinforcing the accuracy of two-step-ahead (2SA) forecasts. The real-time recurrent learning... more
A reliable forecast of future events possesses great value. The main purpose of this paper is to propose an innovative learning technique for reinforcing the accuracy of two-step-ahead (2SA) forecasts. The real-time recurrent learning (RTRL) algorithm for recurrent neural networks (RNNs) can effectively model the dynamics of complex processes and has been used successfully in one-step-ahead forecasts for various time series. A reinforced RTRL algorithm for 2SA forecasts using RNNs is proposed in this paper, and its performance is investigated by two famous benchmark time series and a streamflow during flood events in Taiwan. Results demonstrate that the proposed reinforced 2SA RTRL algorithm for RNNs can adequately forecast the benchmark (theoretical) time series, significantly improve the accuracy of flood forecasts, and effectively reduce time-lag effects.
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We propose a systematical approach to assessing arsenic concentration in a river through: important factor extraction by a nonlinear factor analysis; arsenic concentration estimation by the neuro-fuzzy network; and impact assessment of... more
We propose a systematical approach to assessing arsenic concentration in a river through: important factor extraction by a nonlinear factor analysis; arsenic concentration estimation by the neuro-fuzzy network; and impact assessment of important factors on arsenic concentration by the membership degrees of the constructed neuro-fuzzy network. The arsenic-contaminated Huang Gang Creek in northern Taiwan is used as a study case. Results indicate that rainfall, nitrite nitrogen and temperature are important factors and the proposed estimation model (ANFIS(GT)) is superior to the two comparative models, in which 50% and 52% improvements in RMSE are made over ANFIS(CC) and ANFIS(all), respectively. Results reveal that arsenic concentration reaches the highest in an environment of lower temperature, higher nitrite nitrogen concentration and larger one-month antecedent rainfall; while it reaches the lowest in an environment of higher temperature, lower nitrite nitrogen concentration and sm...
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Research Interests: Environmental Engineering, Civil Engineering, Geology, Computer Science, Artificial Intelligence, and 15 moreMachine Learning, Data Mining, Fuzzy Logic, Neural Networks, Inference, Neural Network, Applied Economics, Flood Forecasting, Learning from experience, Knowledge Extraction, Fuzzy Reasoning, Back Propagation Neural Network, Fuzzy Inference System, Decision Process, and Model performance
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Research Interests: Environmental Science, Remote Sensing, Hydrology, Meteorology, Radar, and 15 moreNeural Networks, Multidisciplinary, Data Structure, Models, Far East, Power Law, Advection, Estimation, Rainfall, Precipitation, Currents, Elsevier, Artificial Neural Network, Quantitative Precipitation Estimation, and remote sensing data
Research Interests: Environmental Toxicology, Ecotoxicology, Arsenic, Bioaccumulation, Biology, and 15 moreAquaculture, Medicine, Biological Sciences, Environmental Sciences, Animals, CHEMICAL SCIENCES, Fresh water, Foot, Health risk, Body Weight, Food Contamination, Oreochromis mossambicus, Acute Toxicity, Environmental, and Oreochromis
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The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles—as well as urban, agricultural, and industrial water cycles—to... more
The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles—as well as urban, agricultural, and industrial water cycles—to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has made notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, non-linear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT...
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Agriculture is extremely vulnerable to climate change. Greenhouse farming is recognized as a promising measure against climate change. Nevertheless, greenhouse farming frequently encounters environmental adversity, especially greenhouses... more
Agriculture is extremely vulnerable to climate change. Greenhouse farming is recognized as a promising measure against climate change. Nevertheless, greenhouse farming frequently encounters environmental adversity, especially greenhouses built to protect against typhoons. Short-term microclimate prediction is challenging because meteorological variables are strongly interconnected and change rapidly. Therefore, this study proposes a water-centric smart microclimate-control system (SMCS) that fuses system dynamics and machine-learning techniques in consideration of the internal hydro-meteorological process to regulate the greenhouse micro-environment within the canopy for environmental cooling with improved resource-use efficiency. SMCS was assessed by in situ data collected from a tomato greenhouse in Taiwan. The results demonstrate that the proposed SMCS could save 66.8% of water and energy (electricity) used for early spraying during the entire cultivation period compared to the t...
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The manuscript addresses the practically important question how regional evaporation (ET) can be estimated from remote sensing data. The authors make use of a highly sophisticated machine learning technique to estimate evaporation from... more
The manuscript addresses the practically important question how regional evaporation (ET) can be estimated from remote sensing data. The authors make use of a highly sophisticated machine learning technique to estimate evaporation from aggregated Landsat images. The authors argue that other remote sensing based ET approaches may fail in heterogeneous terrain such as in Taiwan. While I personally believe that remote sensing based data can actually improve regional ET estimation, the authors got hit by several issues with this approach. So here is a list of scientific concerns which should be addressed by the authors:
This study proposes a multi-objective optimization model of two cascade reservoirs in the Upper Yellow River basin for increasing social well-beings in general while simultaneously mitigating ice/flood threats. We first develop a strategy... more
This study proposes a multi-objective optimization model of two cascade reservoirs in the Upper Yellow River basin for increasing social well-beings in general while simultaneously mitigating ice/flood threats. We first develop a strategy of dimensionality reduction and constraint transformation to largely diminish the complexity of the optimization system and next propose a novel search method that fuses a Feasible Search Space (FSS) into the Particle Swarm Optimization (PSO) algorithm, i.e. FSS-PSO, to effectively solve the optimization problem. To investigate the applicability and effectiveness of the proposed method, this study compares the FSS-PSO model with historical operation. The results indicate that the proposed model produces much better performances in all the objectives than historical operation. To assess the superiority and efficiency of the proposed FSS-PSO, the classical PSO and the Chaos Particle Swarm Optimization (CPSO) are also implemented to compare their comp...
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Research Interests: Computer Science, Artificial Intelligence, Data Assimilation, Artificial Neural Networks, ANN, and 15 moreDisasters, Data Science, Deep Learning, Big Data, Bat Algorithm, Decision Tree, ANFIS, Flash Flood, Bees Algorithm, Database, Artificial Neural Network, Convolutional Neural Networks, Cultural Algorithm, Empirical wavelet transform, and Ensemble Technique
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The dynamic behavior of sodium dodecyl sulfate (SDS)-enhanced solubilization for the removal of trichloroethylene (TCE) from the contaminated soil is studied. Several remediation processes of the TCE-contaminated soil in the columns were... more
The dynamic behavior of sodium dodecyl sulfate (SDS)-enhanced solubilization for the removal of trichloroethylene (TCE) from the contaminated soil is studied. Several remediation processes of the TCE-contaminated soil in the columns were carried out by flushing with the SDS- containing solution of various concentrations under different experimental conditions. The concentration variations of SDS and TCE in the effluent solution were analyzed during the course of the experiments. As a remediation process started, the TCE cumulated on the soil was dissolved gradually as the organic solute into the solution and was removed by the solubilization mechanism. There exist dynamic variations of SDS and TCE concentrations in the solution and residual TCE volumetric fraction on the soil during the remediation process. The case with higher SDS concentration and high flow rate of solution can accomplish the complete remediation faster. In addition, a dynamic axial dispersion model was proposed t...
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Research Interests: Environmental Science, Multivariate Statistics, Hydrology, Principal Component Analysis, Ecohydrology, and 15 moreMultidisciplinary, Multivariate Analysis, Analysis, Identification, Far East, Flow, Reservoir, Land Use, Frequency, Principal Components Analysis, Reservoirs, Drainage Divide, Flow Regime, Multivariate Statistical Methods, and Drainage Basins
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Research Interests: Environmental Science, Hydrology, Principal Component Analysis, Arsenic, Performance, and 15 moreNeural Networks, Multidisciplinary, Concentration, Models, Far East, Model Selection, Ground Water, Principal Components Analysis, Performances, Cross Validation, Aquifers, Spatial Distribution, Sparse Data, Artificial Neural Network, and Coastal Area
Research Interests: Computer Science, Hydrology, Fuzzy Logic, Prediction, Neural Networks, and 15 moreNeural Network, Multidisciplinary, Models, Far East, Reliability, Streamflow, Rainfall, Real Time, Fuzzy Neural Network, Fuzzy System, Runoff, Artificial Neural Network, River Discharge, Optimal Solution, and hydrologic model
Research Interests: Computer Science, Algorithms, Hydrology, Methodology, Neural Networks, and 15 moreNeural Network, Multidisciplinary, Far East, Estuaries, Classification, Nonlinear, Mathematical Models, Accuracy, Multilayer Perceptron, Estuary, Function approximation, Precision, Artificial Neural Network, Drainage Basins, and Multivariate linear regression
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Research Interests: Environmental Engineering, Civil Engineering, Computer Science, Algorithms, Meteorology, and 15 morePrediction, Performance, Neural Networks, Genetic Algorithm, Flood Forecasting, Models, Far East, Flow, Performances, Efficiency, Propagation, Least Squares, Hydrological Processes, Chaotic, and Flood Myth
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Research Interests: Environmental Engineering, Civil Engineering, Computer Science, Recycling, Neural Networks, and 15 moreNeural Network, Flood Forecasting, Models, Far East, Regression, Rainfall, Efficiency, Accuracy, Propagation, Back Propagation Neural Network, Precision, Linear Regression, Artificial Neural Network, Hydrological Processes, and Flood Myth
Research Interests: Engineering, Environmental Engineering, Civil Engineering, Artificial Intelligence, Prediction, and 15 moreOperations Strategy, Performance, Neural Networks, Optimization, Models, Far East, Reservoir Operation, Reservoir, Reservoir Computing, Artificial Intelligent, Intelligent System, Performances, Prediction Model, Evolutionary optimization, and Artificial Neural Network
Research Interests: Environmental Engineering, Civil Engineering, Computer Science, Water resources, Inference, and 15 moreManagement Control, Artificial Neural Networks, Reservoir Operation, Case Study, Prediction Model, Adaptive Network Based Fuzzy Inference System, Hybrid Approach, Water Resource, Neuro Fuzzy, Indexation, Fuzzy Inference System, Heavy Rainfall, Water Level, Artificial Neural Network, and inference system
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water-stage forecasting by using radial basis function