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Application of Machine Learning Techniques in Water Resources Management and...
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Application of Machine Learning Techniques in Water Resources Management and Environmental Engineering

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: 31 May 2024 | Viewed by 7006

Special Issue Editors

Dr. Mehdi Jamei

E-Mail Website
Guest Editor
Faculty of Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan, Iran
Interests: machine learning; water resources management; prediction; forecasting; environmental engineering; hydrological models; time series; water quality systems; hydraulics
Dr. Masoud Karbasi

E-Mail Website
Guest Editor
Water Engineering Department, Faculty of Agriculture, University of Zanjan, Zanjan 45371-38791, Iran
Interests: machine learning; sediment transport; open-channel hydraulics; environmental time series forecasting; deep learning; evapotranspiration; remote sensing

Special Issue Information

Dear Colleagues,

The application of soft computing methods in engineering sciences, particularly water engineering, has received considerable attention in recent years. With their high capacity, these methods can address complex nonlinear engineering problems in the disciplines of regression and classification, and they have gradually replaced traditional mathematical and regression techniques. Soft computing methods are currently utilized extensively in predicting/forecasting hydrological phenomena, various areas of agriculture, and energy; therefore, as computer science advances, their capabilities will increase. This Research Topic aims to publish a broad variety of papers on soft computing and machine learning applications in water science, flood forecasting systems, hydrological and climate research, hydraulic structures, agricultural water management, irrigation scheduling, drought investigations and forecasting, groundwater resources, water resources quality, and environmental engineering. In addition, this Research Topic will provide a venue for researchers, soft computing researchers, and technology developers to present the most recent numerical and computational modeling research on the aforementioned topics.

Dr. Mehdi Jamei
Dr. Masoud Karbasi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • hydrological models
  • prediction
  • forecasting
  • environmental engineering

Published Papers (4 papers)

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Research

16 pages, 3383 KiB  
Article
Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning
by Xuyuan Zhang, Yingqing Guo, Haoran Luo, Tao Liu and Yijun Bao
Water 2024, 16(7), 1018; https://0-doi-org.brum.beds.ac.uk/10.3390/w16071018 - 01 Apr 2024
Viewed by 829
Abstract
The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, [...] Read more.
The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments for soluble chemical oxygen demand (SCOD) and nitrogen degradation for three WWTPs and established machine learning (ML) models for the accurate prediction of the variation in SCOD. The results indicate that four different kinds of components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm and 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes to the majority of internal carbon sources for endogenous denitrification, whereas C4 (230 nm and 275/350 nm, tyrosine-like substances) is crucial for readily biodegradable SCOD composition according to the machine learning (ML) models. Furthermore, the gradient boosting decision tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between SCOD and carbon source components, with an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis of GBDT models further validated the above result. Undoubtedly, this study provided novel insights into utilizing ML models to predict SCOD through the measurements of the excitation–emission matrix (EEM) in specific Ex and Em positions. The results could help us to identify the degradation and transformation relationship between different kinds of carbon sources and nitrogen species in the wastewater treatment process, and thus provide a novel guidance for the optimized operation of WWTPs. Full article
(This article belongs to the Special Issue Application of Machine Learning Techniques in Water Resources Management and Environmental Engineering)
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23 pages, 3418 KiB  
Article
Enhancing Dissolved Oxygen Concentrations Prediction in Water Bodies: A Temporal Transformer Approach with Multi-Site Meteorological Data Graph Embedding
by Hongqing Wang, Lifu Zhang, Rong Wu and Hongying Zhao
Water 2023, 15(17), 3029; https://0-doi-org.brum.beds.ac.uk/10.3390/w15173029 - 23 Aug 2023
Cited by 1 | Viewed by 1443
Abstract
Water ecosystems are highly sensitive to environmental conditions, including meteorological factors, which influence dissolved oxygen (DO) concentrations, a critical indicator of water quality. However, the complex relationships between multiple meteorological factors from various sites and DO concentrations pose a significant challenge for accurate [...] Read more.
Water ecosystems are highly sensitive to environmental conditions, including meteorological factors, which influence dissolved oxygen (DO) concentrations, a critical indicator of water quality. However, the complex relationships between multiple meteorological factors from various sites and DO concentrations pose a significant challenge for accurate prediction. This study introduces an innovative framework for enhancing DO concentration predictions in water bodies by integrating multi-station meteorological data. We first construct a dynamic meteorological graph with station-specific factors as node features and geographic distances as edge weights. This graph is processed using a Geo-Contextual Graph Embedding Module, leveraging a Graph Convolutional Network (GCN) to distill geographical and meteorological features from multi-station data. Extracted features are encoded and then temporally merged with historical DO values to form time-series data. Finally, a Temporal Transformer module is used for future DO concentration predictions. The proposed model shows superior performance compared to traditional methods, successfully capturing the complex relationships between meteorological factors and DO levels. It provides an effective tool for environmental scientists and policymakers in water quality monitoring and management. This study suggests that the integration of graph-based learning and a Temporal Transformer in environmental modeling is a promising direction for future research. Full article
(This article belongs to the Special Issue Application of Machine Learning Techniques in Water Resources Management and Environmental Engineering)
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20 pages, 6003 KiB  
Article
Risk-Based Design Optimization of Contamination Detection Sensors in Water Distribution Systems: Application of an Improved Whale Optimization Algorithm
by Sanaz Afzali Ahmadabadi, Jafar Jafari-Asl, Elham Banifakhr, Essam H. Houssein and Mohamed El Amine Ben Seghier
Water 2023, 15(12), 2217; https://0-doi-org.brum.beds.ac.uk/10.3390/w15122217 - 13 Jun 2023
Cited by 2 | Viewed by 1248
Abstract
In the present study, the optimal placement contamination warning systems (CWSs) in water distribution systems (WDSs) was investigated. To this end, we developed a novel optimization model called WOA-SCSO, which is based on a hybrid nature-inspired algorithm that combines the whale optimization algorithm [...] Read more.
In the present study, the optimal placement contamination warning systems (CWSs) in water distribution systems (WDSs) was investigated. To this end, we developed a novel optimization model called WOA-SCSO, which is based on a hybrid nature-inspired algorithm that combines the whale optimization algorithm (WOA) and sand cat swarm optimization (SCSO). In the proposed hybrid algorithm, the SCSO operators help to find the global optimum solution by preventing the WOA from becoming stuck at a local optimum point. The effectiveness of the WOA-SCSO algorithm was evaluated using the CEC′20 benchmark functions, and the results showed that it outperformed other algorithms, demonstrating its competitiveness. The WOA-SCSO algorithm was finally applied to optimize the locations of CWSs in both a benchmark and a real-world WDS, in order to reduce the risk of contamination. The statistically obtained results of the model implementations on the benchmark WDS showed that the WOA-SCSO had the lowest average and standard deviation of the objective functions in 10 runs, 131,754 m3 and 0, respectively, outperforming the other algorithms. In conclusion, the results of applying the developed optimization model for the optimal placement of CWSs in the Dortmund WDS showed that the worst-case impact risk could be mitigated by 49% with the optimal placement of at least one sensor in the network. These findings suggest that the WOA-SCSO algorithm can serve as an effective optimization tool, particularly for determining the optimal placements of CWSs in WDSs. Full article
(This article belongs to the Special Issue Application of Machine Learning Techniques in Water Resources Management and Environmental Engineering)
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33 pages, 88977 KiB  
Article
Deep Learning Approaches for Numerical Modeling and Historical Reconstruction of Water Quality Parameters in Lower Seine
by Imad Janbain, Abderrahim Jardani, Julien Deloffre and Nicolas Massei
Water 2023, 15(9), 1773; https://0-doi-org.brum.beds.ac.uk/10.3390/w15091773 - 05 May 2023
Viewed by 1971
Abstract
Water quality monitoring is essential for managing water resources and ensuring human and environmental health. However, obtaining reliable data can be challenging and costly, especially in complex systems such as estuaries. To address this problem, we propose a novel deep learning-based approach that [...] Read more.
Water quality monitoring is essential for managing water resources and ensuring human and environmental health. However, obtaining reliable data can be challenging and costly, especially in complex systems such as estuaries. To address this problem, we propose a novel deep learning-based approach that uses limited available data to accurately estimate and reconstruct critical water quality variables, such as electrical conductivity, dissolved oxygen, and turbidity. Our approach included two tasks, numerical modeling and historical reconstruction, and was applied to the Seine River in the Normandy region of France at four quality stations. In the first task, we evaluated four deep learning approaches (GRU, BiLSTM, BiLSTM-Attention, and CNN-BiLSTM-Attention) to numerically simulate each variable for each station under different input data selection scenarios. We found that incorporating the quality data with the water level data collected at the various stations into the input data improved the accuracy of the water quality data simulation. Combining water levels from multiple stations reliably reproduced electrical conductivity, especially at stations near the sea where tidal fluctuations control saltwater intrusion in the area. While each model had its strengths, the CNN-BiLSTM-Attention model performed best in complex tasks with dissimilar input trends, and the GRU model outperformed other models in simple monitoring tasks with similar input-target trends. The second task involved automatically searching the optimal configurations for completing the missing historical data in sequential order using the modeling task results. The electrical conductivity data were filled before the dissolved oxygen data, which were in turn more reliable than the turbidity simulation. The deep learning models accurately reconstructed 15 years of water quality data using only six and a half years of modeling data. Overall, this research demonstrates the potential of deep learning approaches with their limitations and discusses the best configurations to improve water quality monitoring and reconstruction. Full article
(This article belongs to the Special Issue Application of Machine Learning Techniques in Water Resources Management and Environmental Engineering)
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