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Machine Learning Applications in the Water Domain

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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: 25 September 2025 | Viewed by 3539

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Special Issue Editor

Dr. Yonggui Wang

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Guest Editor

Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: pollution source identification; risk assessment of water quality; water environment modelling; Artificial Intelligence; water environment management systems
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Special Issue Information

Dear Colleagues,

Water is an indispensable resource for life, and its management and monitoring are crucial for sustainable development. The advent of Machine Learning (ML) has revolutionized the way we analyze and predict hydrological phenomena and water quality. This Special Issue aims to showcase the latest advancements in the application of ML in the field of water, particularly focusing on the prediction of hydrological processes and water quality, as well as the application of machine vision in water bodies. We invite researchers to submit their latest findings on the following topics:

Applications of machine learning models in time series predictions of hydrology and water quality;
Applications of and key issues in machine learning in water body identification and classification;
Comparative analysis of different machine learning models;
Research on the coupling of knowledge and data;
Related research on the interpretability, performance evaluation, and other aspects of machine learning.

Dr. Yonggui Wang
Guest Editor

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

water resource
machine learning (ML)
neural network
hydrological phenomena
water quality
time series prediction
water body identification
classification
comparative analysis
interpretability of ML

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Published Papers (5 papers)

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Research

23 pages, 4325 KB

Open AccessArticle

Groundwater Level Estimation Using Improved Transformer Model: A Case Study of the Yellow River Basin

by Tianming Zhou, Chun Fu, Yezhong Liu and Libin Xiang

Water 2025, 17(15), 2318; https://doi.org/10.3390/w17152318 - 4 Aug 2025

Viewed by 428

Abstract

Accurate estimation of groundwater levels in river basins is essential for effective water resource planning. Innovations in deep learning and artificial intelligence (AI) have been introduced into this field to enhance the accuracy of long-term groundwater level estimation. This study employs the Transformer deep learning model to estimate groundwater levels, with a benchmark comparison against the long short-term memory (LSTM) model. These models were applied to estimate groundwater levels in the Yellow River Basin, where approximately 1100 monitoring wells are located. Monthly average groundwater level data from the period 2018–2023 were collected from these wells. The two models were used to estimate groundwater levels for the period 2003–2017 by incorporating remote sensing information. The Transformer model was enhanced to simultaneously capture features from both historical temporal data and surrounding spatial data, while automatically enhancing key features, effectively improving estimation accuracy and robustness. At the basin-averaged scale, the enhanced Transformer model outperformed the LSTM model: R² increased by approximately 17.5%, while RMSE and MAE decreased by approximately 12.4% and 10.9%, respectively. The proportion of poorly predicted samples decreased by an average of approximately 12.1%. The estimation model established in this study contributes to improving the quantitative analysis capability of long-term groundwater level variations in the Yellow River Basin. This could be helpful for water resource development planning in this densely populated region and likely has broad applicability in other river basins. Full article

(This article belongs to the Special Issue Machine Learning Applications in the Water Domain)

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24 pages, 3000 KB

Open AccessArticle

Identifying Worst Transient Cases and Optimizing Surge Protection for Existing Water Networks

by Hossam Mohamed Ahmed, Yehya Emad Imam, Hamdy Ahmed El-Ghandour and Amgad Saad Elansary

Water 2025, 17(12), 1816; https://doi.org/10.3390/w17121816 - 17 Jun 2025

Viewed by 525

Abstract

Previous studies of transients in existing water distribution networks (WDNs) accounted for only single worst cases in optimizing surge protection measures, considered only pressure at pipe end nodes, and did not examine the effect of regulating the duration of demand increase. This study presents a comprehensive model for identifying the minimal set of worst transient cases for which optimized surge protection achieves zero Surge Damage Potential Factor (SDPF) for all transient loading cases. The model introduces

S D P F_{P}

to account for pressure at all computational nodes along pipes, as opposed to relying on

S D P F_{N}

, which considers pressure at pipe end nodes only. The existing New York Tunnel network was used for model validation and for determining the optimal diameters for additional duplicate pipes to achieve higher pressure under steady-state conditions and protect the network from transients due to demand increase. Compared to previous studies, the model achieved

S D P F_{N} = 0

with a lower cost for sudden demand increase at a single predefined node. For concurrent sudden demand increase at two nodes, the model identified a total of 8 critical transient cases and corresponding optimum duplicate pipe diameters that achieved

S D P F_{N} = 0

and

S D P F_{P} = 0

with 46% and 74% higher costs than previous studies, respectively. The higher costs are necessary; previous studies did not achieve zero

S D P F_{N}

and

S D P F_{P}

in 39% and 91% of transient cases, respectively. To reduce duplicate pipe costs, the model was used to examine the effect of regulating the duration for a gradual demand increase. Using only the pipes optimized for steady-state service, the minimum duration for satisfying the transient pressure constraints was identified as ~260 s for the concurrent demand increase scenario. Slight relaxation of the minimum allowable pressure constraint allows a reduction in the duration to 150 s. For applying a demand increase over a smaller duration, duplicate pipes would be needed and can be optimized using the model. These results indicate the advantage of the proposed model in achieving full protection of existing WDNs while maintaining computational efficiency and cost-effectiveness. Full article

(This article belongs to the Special Issue Machine Learning Applications in the Water Domain)

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16 pages, 2123 KB

Open AccessArticle

Improved Reinforcement Learning for Multi-Objective Optimization Operation of Cascade Reservoir System Based on Monotonic Property

by Xiang Li, Haoyu Ma, Sitong Chen, Yang Xu and Xiang Zeng

Water 2025, 17(11), 1681; https://doi.org/10.3390/w17111681 - 2 Jun 2025

Viewed by 549

Abstract

In this paper, improved reinforcement learning (IRL) is designed for the multi-objective optimization operation of a cascade reservoir system. The primary improvement of IRL is searching within limited solution space, based on the derived monotonic property: the first-order derivative relationship between individual reservoir water release decisions for mainstream use (i.e., hydropower generation) as well as tributary use (i.e., regional water supply) and the cascade system’s or a particular reservoir’s water availability, along with the synchronicity and substitutability assumption of storage distribution in the cascade system. The improved algorithm is then applied to a real-world cascade reservoir system in the Yangtze River of China. The results demonstrate the high computational efficiency and reasonable interpretability of IRL. Full article

(This article belongs to the Special Issue Machine Learning Applications in the Water Domain)

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29 pages, 16679 KB

Open AccessArticle

Advancing Ion Constituent Simulations in California’s Sacramento–San Joaquin Delta Using Machine Learning Tools

by Peyman Namadi, Minxue He and Prabhjot Sandhu

Water 2025, 17(10), 1511; https://doi.org/10.3390/w17101511 - 16 May 2025

Viewed by 654

Abstract

This study extends previous machine learning work on ion constituent simulation in California’s Sacramento–San Joaquin Delta (Delta) to include three critical water intake locations. The developed Artificial Neural Network models demonstrate exceptional accuracy (R² > 0.96) in predicting chloride, bromide, and sulfate concentrations at these strategically important facilities. Water intake location models show substantial improvements in prediction accuracy, with MAE reductions of 60.7–74.0% for chloride, 63.3–72.5% for bromide, and 70.4–87.9% for sulfate, compared to existing methods for the Interior Delta. Performance evaluation through comprehensive cross-validation confirms robust model stability across varied conditions, with remarkably consistent metrics (standard deviation in R² ≤ 0.006). Four complementary interactive dashboards were developed, enabling users, regardless of programming expertise, to simulate ion constituents throughout the Delta system. A Model Interpretability Dashboard specifically addresses the complexity of machine learning models by visualizing parameter sensitivity and prediction behavior, thereby enhancing transparency and building stakeholder trust in the modeling approach. For the first time, spatial coverage limitations are addressed through hybrid modeling that combines DSM2 hydrodynamic simulation with machine learning to enable continuous prediction of ion distributions across several points in the Interior Delta. These advancements provide water managers with accessible, accurate tools for informed decision-making regarding agricultural operations, drinking water treatment, and ecosystem management in this vital water resource. Full article

(This article belongs to the Special Issue Machine Learning Applications in the Water Domain)

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18 pages, 7849 KB

Open AccessArticle

Analysis of Prediction Confidence in Water Quality Forecasting Employing LSTM

by Pan Fang, Yonggui Wang, Yanxin Zhao and Jin Kang

Water 2025, 17(7), 1050; https://doi.org/10.3390/w17071050 - 2 Apr 2025

Cited by 2 | Viewed by 1002

Abstract

Water quality prediction serves as an important foundation for risk control and the proactive management of the aquatic environment, and the Long Short-Term Memory (LSTM) network has gained recognition as an effective approach for achieving high-precision water quality predictions. However, despite its potential, there is a significant gap in the literature regarding the confidence analysis of its prediction accuracy and the underlying causes of variability across different water quality indicators and basins. To address this gap, the present study introduces a novel confidence evaluation method to systematically assess the performance of LSTM in predicting key water quality parameters, including ammonia nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), hydrogen ion concentration (pH), and total phosphorus (TP). This evaluation was conducted across three basins with distinct geographical, climatic, and water quality conditions: the Huangshui River Basin (HSB), the Haihe River Basin (HRB), and the Yangtze River Basin (YRB). The results of the confidence evaluation revealed that LSTM exhibited higher credibility in the Haihe River Basin compared to the Yangtze River Basin. Additionally, LSTM demonstrated greater accuracy and stability in predicting total phosphorus (TP) compared to other water quality indicators in both basins, with median NSE values of 0.71 in the HRB and 0.73 in the YRB. Additionally, the research demonstrated a linear relationship between the ability of LSTM models to predict the water quality and temporal autocorrelation as well as the cross-correlation coefficients of the water quality parameters. The coefficients of determination (R²) ranged from 0.59 to 0.85, with values of 0.59 and 0.79 for the YRB and 0.85 and 0.80 for the HRB, respectively. This finding underscores the importance of considering these correlation metrics when evaluating the reliability of LSTM-based predictions. Full article

(This article belongs to the Special Issue Machine Learning Applications in the Water Domain)

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