Application of Machine Learning to Water Resource Modeling

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 14446

Special Issue Editor

Collage of Water Resource and Architectural Engineering, Northwest A&F University, Xianyang, China
Interests: machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water shortages have become a severe problem in many parts of the world. Although a variety of artificial interventions have been used to deal with water shortages, they have brought a series of ecological and environmental problems such as groundwater table decline, vegetation degradation, land desertification, and water quality deterioration, posing great challenges for water resource management. In recent decades, a series of models and tools have been developed for water resource simulation, optimization, and management. However, with the continuous intensification of human activities, the water resource system has gradually become a typical “human–natural coupling system” which interacts with human activities and natural processes. The complex interaction and nonlinear relationship between human systems and natural systems make these natural system-based water resource management tools and models unable to accurately capture and reflect the evolution process of the water resources system. Therefore, the question of how to analyze the water resources system comprehensively and systematically and accurately describe the hydrology and water cycle process under the dual drive of the human system and the natural system has become a hot spot and difficult problem in the current water resource management research. This Special Issue aims to collect and share innovative ideas and applications of machine learning to water resource modeling. We are pleased to invite authors to publish original research articles, review articles, and short communications on relevant topics.

Dr. Ze Liu
Guest Editor

Manuscript Submission Information

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Keywords

  • water resources systems
  • monitoring, remediation, and protection of water resources
  • water resources planning
  • adaptive management
  • water demand management
  • national and international water policy
  • water economics

Published Papers (6 papers)

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Research

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17 pages, 2909 KiB  
Article
Using Clustering, Geochemical Modeling, and a Decision Tree for the Hydrogeochemical Characterization of Groundwater in an In Situ Leaching Uranium Deposit in Bayan-Uul, Northern China
by Haibo Li, Mengqi Liu, Tian Jiao, Dongjin Xiang, Xiaofei Yan, Zhonghua Tang and Jing Yang
Water 2023, 15(24), 4234; https://doi.org/10.3390/w15244234 - 8 Dec 2023
Cited by 2 | Viewed by 1244
Abstract
Uranium extraction through the in situ leaching method stands as a pivotal approach in uranium mining. In an effort to comprehensively assess the repercussions of in situ uranium leaching on groundwater quality, this study collected 12 representative groundwater samples within the Bayan-Uul mining [...] Read more.
Uranium extraction through the in situ leaching method stands as a pivotal approach in uranium mining. In an effort to comprehensively assess the repercussions of in situ uranium leaching on groundwater quality, this study collected 12 representative groundwater samples within the Bayan-Uul mining area. The basic statistical characteristics of the water samples showed that the concentrations of SO42− and total dissolved solids (TDS) were relatively high. Through the use of cluster analysis, the water samples were categorized into two distinct clusters. Seven samples from wells W-d, W-u, N01, W10-2, W08-1, W10-1, and W13-1, situated at a considerable distance from the mining area, were grouped together. Conversely, five samples from wells W08-2, W13-2, W01-1, W02-2, and the pumping well located in closer proximity to the mining area, formed a separate cluster. A decision tree-based machine learning approach was employed to discern the influence of various hydrochemical indicators in forming these clusters, with results indicating that SO42− exerts the most substantial influence, followed by Ca2+. The mineral saturation indices from geochemical modeling indicated that, as the distance from the mining area increased, the trend of calcium minerals changed from dissolution to precipitation; iron minerals were in a precipitation state, and the precipitation trend was gradually weakening. In light of these findings, it is clear that in situ uranium leaching significantly impacted the groundwater in the vicinity of the mining area. The prolonged consumption of groundwater sourced near the study area, or its use for animal husbandry, poses potential health risks that demand heightened attention. Full article
(This article belongs to the Special Issue Application of Machine Learning to Water Resource Modeling)
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16 pages, 7323 KiB  
Article
Using Self-Organizing Map and Multivariate Statistical Methods for Groundwater Quality Assessment in the Urban Area of Linyi City, China
by Shiqiang Liu, Haibo Li, Jing Yang, Mingqiang Ma, Jiale Shang, Zhonghua Tang and Geng Liu
Water 2023, 15(19), 3463; https://doi.org/10.3390/w15193463 - 30 Sep 2023
Cited by 1 | Viewed by 1264
Abstract
Groundwater holds an important role in the water supply in Linyi city, China. Investigating the hydrochemical characteristics of groundwater, and revealing the factors governing groundwater geochemistry, is a primary step for ensuring the safe and rational exploitation of groundwater resources. This study used [...] Read more.
Groundwater holds an important role in the water supply in Linyi city, China. Investigating the hydrochemical characteristics of groundwater, and revealing the factors governing groundwater geochemistry, is a primary step for ensuring the safe and rational exploitation of groundwater resources. This study used a self-organizing map (SOM) and multivariate statistical methods to assess groundwater quality in the urban area of Linyi city. Based on the hydrochemical dataset consisting of nine parameters (i.e., pH, Ca2+, Mg2+, Na+, K+, HCO3, Cl, SO42, and NO3) from 89 groundwater samples, the SOM was first applied to obtain the weight vectors of the output nodes. Hierarchical cluster analysis (HCA) was used for organizing the nodes into four clusters. The node cluster indices were then remapped to the groundwater samples according to the winner node for each sample. The hydrochemical characteristics and factors controlling the groundwater geochemistry of the four clusters were analyzed using principal component analysis (PCA) and graphical methods including Piper and Gibbs diagrams, as well as binary plots of the major ions in groundwater. Results indicated that groundwater geochemistry in this area is primarily governed by water–rock interactions, such as the dissolution of halite, calcite, and gypsum, along with the influence of municipal sewage and the degradation of organic matter. This study demonstrates that the integration of an SOM and multivariate statistical methods improves the understanding of groundwater geochemistry and hydrochemical evolution in complex groundwater flow systems impacted by utilization. Full article
(This article belongs to the Special Issue Application of Machine Learning to Water Resource Modeling)
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28 pages, 5971 KiB  
Article
Enhancing Water Temperature Prediction in Stratified Reservoirs: A Process-Guided Deep Learning Approach
by Sungjin Kim and Sewoong Chung
Water 2023, 15(17), 3096; https://doi.org/10.3390/w15173096 - 29 Aug 2023
Cited by 1 | Viewed by 1417
Abstract
Data-driven models (DDMs) are extensively used in environmental modeling yet encounter obstacles stemming from limited training data and potential discrepancies with physical laws. To address this challenge, this study developed a process-guided deep learning (PGDL) model, integrating a long short-term memory (LSTM) neural [...] Read more.
Data-driven models (DDMs) are extensively used in environmental modeling yet encounter obstacles stemming from limited training data and potential discrepancies with physical laws. To address this challenge, this study developed a process-guided deep learning (PGDL) model, integrating a long short-term memory (LSTM) neural network and a process-based model (PBM), CE-QUAL-W2 (W2), to predict water temperature in a stratified reservoir. The PGDL model incorporates an energy constraint term derived from W2′s thermal energy equilibrium into the LSTM’s cost function, alongside the mean square error term. Through this mechanism, PGDL optimizes parameters while penalizing deviations from the energy law, thereby ensuring adherence to crucial physical constraints. In comparison to LSTM’s root mean square error (RMSE) of 0.062 °C, PGDL exhibits a noteworthy 1.5-fold enhancement in water temperature prediction (RMSE of 0.042 °C), coupled with improved satisfaction in maintaining energy balance. Intriguingly, even with training on just 20% of field data, PGDL (RMSE of 0.078 °C) outperforms both LSTM (RMSE of 0.131 °C) and calibrated W2 (RMSE of 1.781 °C) following pre-training with 80% of the data generated by the uncalibrated W2 model. The successful integration of the PBM and DDM in the PGDL validates a novel technique that capitalizes on the strengths of multidimensional mathematical models and data-based deep learning models. Furthermore, the pre-training of PGDL with PBM data demonstrates a highly effective strategy for mitigating bias and variance arising from insufficient field measurement data. Full article
(This article belongs to the Special Issue Application of Machine Learning to Water Resource Modeling)
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12 pages, 3373 KiB  
Article
Simulation of Water Level and Flow of Catastrophic Flood Based on the CNN-LSTM Coupling Network
by Yang Xu, Chao He, Zhengqiang Guo, Yanfei Chen, Yongxi Sun and Yuru Dong
Water 2023, 15(13), 2329; https://doi.org/10.3390/w15132329 - 22 Jun 2023
Cited by 1 | Viewed by 1501
Abstract
The occurrence of catastrophic floods will increase the uncertainty of hydrological forecasting at downstream hydrological stations. In order to solve the problems of the unclear propagation law of catastrophic floods in the middle and lower reaches of the Yangtze River and the inadaptability [...] Read more.
The occurrence of catastrophic floods will increase the uncertainty of hydrological forecasting at downstream hydrological stations. In order to solve the problems of the unclear propagation law of catastrophic floods in the middle and lower reaches of the Yangtze River and the inadaptability of traditional forecasting methods, this paper uses the M-K trend test method to analyze the annual average flow and annual average water level of the Yichang and Hankou stations. For conventional floods and catastrophic floods, Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), and CNN-LSTM neural networks are used to simulate the water level/flow of Hankou station. The simulation results are analyzed by Nash–Sutcliffe Efficiency Coefficient (NSE), Kling–Gupta efficiency coefficient (KGE), Root Mean Square Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results show that the annual average flow and annual average water level of Yichang station show a downward trend and the annual average water level of Hankou station shows an upward trend. By comparing the four indicators of NSE, KGE, RMSE, and SMAPE, the CNN-LSTM coupling model was determined to be the best fitting model, with NSE and KGE greater than 0.995 and RMSE and SMAPE less than 0.200. The proposed coupling model can provide technical support for flood control optimization, scheduling, emergency rescue, and scheduling impact analysis of the Three Gorges Power Station. Full article
(This article belongs to the Special Issue Application of Machine Learning to Water Resource Modeling)
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Review

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26 pages, 1130 KiB  
Review
Research on Water Resource Modeling Based on Machine Learning Technologies
by Ze Liu, Jingzhao Zhou, Xiaoyang Yang, Zechuan Zhao and Yang Lv
Water 2024, 16(3), 472; https://doi.org/10.3390/w16030472 - 31 Jan 2024
Cited by 1 | Viewed by 2697
Abstract
Water resource modeling is an important means of studying the distribution, change, utilization, and management of water resources. By establishing various models, water resources can be quantitatively described and predicted, providing a scientific basis for water resource management, protection, and planning. Traditional hydrological [...] Read more.
Water resource modeling is an important means of studying the distribution, change, utilization, and management of water resources. By establishing various models, water resources can be quantitatively described and predicted, providing a scientific basis for water resource management, protection, and planning. Traditional hydrological observation methods, often reliant on experience and statistical methods, are time-consuming and labor-intensive, frequently resulting in predictions of limited accuracy. However, machine learning technologies enhance the efficiency and sustainability of water resource modeling by analyzing extensive hydrogeological data, thereby improving predictions and optimizing water resource utilization and allocation. This review investigates the application of machine learning for predicting various aspects, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. It provides a detailed summary of various algorithms, examines their technical strengths and weaknesses, and discusses their potential applications in water resource modeling. Finally, this paper anticipates future development trends in the application of machine learning to water resource modeling. Full article
(This article belongs to the Special Issue Application of Machine Learning to Water Resource Modeling)
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30 pages, 3189 KiB  
Review
Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review
by Muhammad Waqas, Usa Wannasingha Humphries, Angkool Wangwongchai, Porntip Dechpichai and Shakeel Ahmad
Water 2023, 15(16), 2979; https://doi.org/10.3390/w15162979 - 18 Aug 2023
Cited by 7 | Viewed by 5474
Abstract
Rainfall forecasting is one of the most challenging factors of weather forecasting all over the planet. Due to climate change, Thailand has experienced extreme weather events, including prolonged lacks of and heavy rainfall. Accurate rainfall forecasting is crucial for Thailand’s agricultural sector. Agriculture [...] Read more.
Rainfall forecasting is one of the most challenging factors of weather forecasting all over the planet. Due to climate change, Thailand has experienced extreme weather events, including prolonged lacks of and heavy rainfall. Accurate rainfall forecasting is crucial for Thailand’s agricultural sector. Agriculture depends on rainfall water, which is important for water resources, adversity management, and overall socio-economic development. Artificial intelligence techniques (AITs) have shown remarkable precision in rainfall forecasting in the past two decades. AITs may accurately forecast rainfall by identifying hidden patterns from past weather data features. This research investigates and reviews the most recent AITs focused on advanced machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) utilized for rainfall forecasting. For this investigation, academic articles from credible online search libraries published between 2000 and 2022 are analyzed. The authors focus on Thailand and the worldwide applications of AITs for rainfall forecasting and determine the best methods for Thailand. This will assist academics in analyzing the most recent work on rainfall forecasting, with a particular emphasis on AITs, but it will also serve as a benchmark for future comparisons. The investigation concludes that hybrid models combining ANNs with wavelet transformation and bootstrapping can improve the current accuracy of rainfall forecasting in Thailand. Full article
(This article belongs to the Special Issue Application of Machine Learning to Water Resource Modeling)
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