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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: closed (20 August 2025) | Viewed by 23252

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Dr. Gonzalo Astray

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Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain
Interests: artificial intelligence (neural networks, fuzzy logic, expert systems, etc.); physical chemistry; water management; hydrology; food technology; bioinformatics; palynology
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Dr. Diego Fernández-Nóvoa

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Environmental Physics Laboratory (EPhysLab), Centro de Investigación Mariña (CIM), Universidade de Vigo, Campus da Auga, 32004 Ourense, Spain
Interests: hydrodynamic numerical simulation; artificial intelligence; flood analysis; flood forecasting; flood early warning systems; climate change
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Special Issue Information

Dear Colleagues,

In the last years, applications based on machine learning (ML) have been widely used to solve problems in different scientific areas. Within the current ML algorithms, support vector machines, Bayesian networks, and artificial neural networks, among others, can be mentioned.

Currently, there are many monitoring instruments/stations that allow a daily collection of hydrological data. Different ML-based models can be fed with these data to study/model the following: dam/water supply management, extreme events, natural/anthropogenic changes in lakes, transport of pollutants, drinking water quality, landslides induced by rain, etc.

The objective of this Special Issue on “Application of Machine Learning in Hydrologic Sciences” is to present current research on the aforementioned problems (but not limited exclusively to them) using machine learning.

We invite all researchers, working in hydrological sciences and ML, to submit research or review articles that demonstrate the significant potential of machine learning in this field.

Dr. Gonzalo Astray
Dr. Diego Fernández-Nóvoa
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 250 words) can be sent to the Editorial Office for assessment.

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

hydrology
water cycle
water–soil–atmosphere
machine learning
big data
monitoring/modelling/prediction/optimization/management
water flow/quality/supply/energy
risk/hazard assessment
multidisciplinary water research

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Related Special Issue

Application of Machine Learning in Hydrologic Sciences, 2nd Edition in Water (1 article)

Published Papers (7 papers)

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Research

Jump to: Review

39 pages, 9543 KB

Open AccessArticle

A Hybrid PCA-TOPSIS and Machine Learning Approach to Basin Prioritization for Sustainable Land and Water Management

by Mustafa Aytekin, Semih Ediş and İbrahim Kaya

Water 2026, 18(1), 5; https://doi.org/10.3390/w18010005 - 19 Dec 2025

Cited by 1 | Viewed by 958

Abstract

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences)

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17 pages, 3375 KB

Open AccessArticle

Assessing Climate Change and Reservoir Impacts on Upper Miño River Flow (NW Iberian Peninsula) Using Neural Networks

by Helena Barreiro-Fonta and Diego Fernández-Nóvoa

Water 2025, 17(24), 3514; https://doi.org/10.3390/w17243514 - 12 Dec 2025

Viewed by 661

Abstract

Climate change is altering the global hydrological cycle, which, combined with human interventions, such as reservoir operation, further disrupts river flows. Given the heterogeneity and importance of these impacts, and the particularities of each basin, regional studies are essential to assess local vulnerabilities. This study focuses on the upper Miño basin (NW Iberian Peninsula), together with the Belesar reservoir, to evaluate projected changes in streamflow between historical (1985–2014) and future (2070–2099) periods under the SSP5-8.5 and the SSP2-4.5 scenarios. Neural networks were applied to model the hydrological cycle, estimating flow from temperature and precipitation data, as well as to simulate reservoir operation, achieving successful validation. Results for SSP5-8.5 reveal a projected intensification of the hydrological cycle, with the 10th percentile (defining low-flow conditions) projected to decrease by approximately −10%, while the 99.997th percentile (defining high-flow conditions) is expected to increase by about +5%. Mean streamflow is projected to decline by more than −15%. Under the more moderate SSP2-4.5 scenario, changes are less pronounced, with the low-flow percentile expected to decrease by roughly −5% and mean streamflow showing a projected decline not reaching −15%. In contrast, the high-flow percentile exhibits an opposite trend, with a projected decrease of about −30% relative to the historical period. The analysis of reservoir operation was conducted under the most extreme emission scenario (SSP5-8.5), to assess its regulatory capacity under the harshest projected hydrological conditions. Results show that reservoir operation helps moderate the projected impact by redistributing water from wetter to drier periods, more than doubling projected summer flows downstream relative to upstream, and lowering winter flows, with the one-year return period value (99.997th percentile) projected to be reduced by approximately −15% by reservoir operation. Although natural future conditions are projected to become more critical, both the adoption of a more moderate emission pathway and an adequate reservoir operation will contribute to alleviating the most adverse hydrological impacts. Full article

(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences)

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29 pages, 5375 KB

Open AccessArticle

Application of PINNs to Define Roughness Coefficients for Channel Flow Problems

by Sergei Strijhak, Konstantin Koshelev and Andrei Bolotov

Water 2025, 17(18), 2731; https://doi.org/10.3390/w17182731 - 16 Sep 2025

Cited by 1 | Viewed by 3210

Abstract

This paper considers the possibility of using Physics-Informed Neural Networks (PINNs) to study the hydrological processes of model river sections. A fully connected neural network is used for the approximation of the Saint-Venant equations in both 1D and 2D formulations. This study addresses the problem of determining the velocities, water level, discharge, and area of water sections in 1D cases, as well as the inverse problem of calculating the roughness coefficient. To evaluate the applicability of PINNs for modeling flows in channels, it seems reasonable to start with cases where exact reference solutions are available. For the 1D case, we examined a rectangular channel with a given length, width, and constant roughness coefficient. An analytical solution is obtained to calculate the discharge and area of the water section. Two-dimensional model examples were also examined. The synthetic data were generated in Delft3D code, which included velocity field and water level, for the purpose of PINN training. The calculation in Delft3D code took about 2 min. The influence of PINN hyperparameters on the prediction quality was studied. Finally, the absolute error value was assessed. The prediction error of the roughness coefficient n value in the 2D case for the inverse problem did not exceed 10%. A typical training process took from 2.5 to 3.5 h and the prediction process took 5–10 s using developed PINN models on a server with Nvidia A100 40GB GPU. Full article

(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences)

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24 pages, 6724 KB

Open AccessArticle

Long-Lead-Time Typhoon Wave Prediction Using Data-Driven Models, Typhoon Parameters, and Geometric Effective Factors on the Northwest Coast of Taiwan

by Wei-Ting Chao

Water 2025, 17(9), 1376; https://doi.org/10.3390/w17091376 - 2 May 2025

Cited by 1 | Viewed by 3166

Abstract

This study introduces an innovative long-lead-time prediction model for typhoon-induced waves through the back-propagation neural network (BPNN) method along Taiwan’s northwest coast, a region vulnerable to severe coastal hazards due to its exposure to frequent typhoons and ongoing offshore energy development. Utilizing data from 13 typhoons (2001–2024) at the Hsinchu buoy station, the model integrates nine parameters—including significant wave height, typhoon parameters (e.g., wind speed, central pressure), and novel geometric factors like topographic elevation—to enhance forecast accuracy. The proposed WVPDUG model, incorporating forward speed, movement direction, and topography, outperforms traditional approaches, achieving over 60% improvement in RMSE and CC for lead times up to 10 h. A knowledge extraction method (KEM) further unveils the neural network’s internal dynamics, offering unprecedented insight into parameter contributions. This research addresses a critical gap in long-term wave forecasting under complex topographic influences, providing a robust tool for early warning systems and coastal disaster mitigation in typhoon-prone regions. Full article

(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences)

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29 pages, 5371 KB

Open AccessEditor’s ChoiceArticle

Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western U.S. Watersheds

by Junjie Chen and Heejun Chang

Water 2025, 17(3), 359; https://doi.org/10.3390/w17030359 - 27 Jan 2025

Cited by 8 | Viewed by 3104

Abstract

Wildfires significantly impact water quality in the Western United States, posing challenges for water resource management. However, limited research quantifies post-wildfire stream temperature and turbidity changes across diverse climatic zones. This study addresses this gap by using Random Forest (RF) and Support Vector Regression (SVR) models to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data from the Clackamas and Russian River Watersheds. We selected Random Forest (RF) and Support Vector Regression (SVR) because they handle non-linear, high-dimensional data, balance accuracy with efficiency, and capture complex post-wildfire stream temperature and turbidity dynamics with minimal assumptions. The primary objectives were to evaluate model performance, conduct sensitivity analyses, and project mid-21st century water quality changes under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Sensitivity analyses indicated that 7-day maximum air temperature and discharge were the most influential predictors. Results show that RF outperformed SVR, achieving an R² of 0.98 and root mean square error of 0.88 °C for stream temperature predictions. Post-wildfire turbidity increased up to 70 NTU during storm events in highly burned subwatersheds. Under RCP 8.5, stream temperatures are projected to rise by 2.2 °C by 2050. RF’s ensemble approach captured non-linear relationships effectively, while SVR excelled in high-dimensional datasets but struggled with temporal variability. These findings underscore the importance of using machine learning for understanding complex post-fire hydrology. We recommend adaptive reservoir operations and targeted riparian restoration to mitigate warming trends. This research highlights machine learning’s utility for predicting post-wildfire impacts and informing climate-resilient water management strategies. Full article

(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences)

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21 pages, 3527 KB

Open AccessEditor’s ChoiceArticle

Quantifying Predictive Uncertainty and Feature Selection in River Bed Load Estimation: A Multi-Model Machine Learning Approach with Particle Swarm Optimization

by Xuan-Hien Le, Trung Tin Huynh, Mingeun Song and Giha Lee

Water 2024, 16(14), 1945; https://doi.org/10.3390/w16141945 - 10 Jul 2024

Cited by 14 | Viewed by 2668

Abstract

This study presents a comprehensive multi-model machine learning (ML) approach to predict river bed load, addressing the challenge of quantifying predictive uncertainty in fluvial geomorphology. Six ML models—random forest (RF), categorical boosting (CAT), extra tree regression (ETR), gradient boosting machine (GBM), Bayesian regression model (BRM), and K-nearest neighbors (KNNs)—were thoroughly evaluated across several performance metrics like root mean square error (RMSE), and correlation coefficient (R). To enhance model training and optimize performance, particle swarm optimization (PSO) was employed for hyperparameter tuning across all the models, leveraging its capability to efficiently explore complex hyperparameter spaces. Our findings indicated that RF, GBM, CAT, and ETR demonstrate superior predictive performance (R score > 0.936), benefiting significantly from PSO. In contrast, BRM displayed lower performance (0.838), indicating challenges with Bayesian approaches. The feature importance analysis, including permutation feature and SHAP values, highlighted the non-linear interdependencies between the variables, with river discharge (Q), bed slope (S), and flow width (W) being the most influential. This study also examined the specific impact of individual variables on model performance by adding and excluding individual variables, which is particularly meaningful when choosing input variables for the model, especially in limited data conditions. Uncertainty quantification through Monte Carlo simulations highlighted the enhanced predictability and reliability of models with larger datasets. The correlation between increased training data and improved model precision was evident in the consistent rise in mean R scores and reduction in standard deviations as the sample size increased. This research underscored the potential of advanced ensemble methods and PSO to mitigate the limitations of single-predictor models and exploit collective model strengths, thereby improving the reliability of predictions in river bed load estimation. The insights from this study provide a valuable framework for future research directions focused on optimizing ensemble configurations for hydro-dynamic modeling. Full article

(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences)

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Review

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37 pages, 3679 KB

Open AccessReview

Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review

by Jerome G. Gacu, Cris Edward F. Monjardin, Ronald Gabriel T. Mangulabnan and Jerime Chris F. Mendez

Water 2025, 17(18), 2722; https://doi.org/10.3390/w17182722 - 14 Sep 2025

Cited by 19 | Viewed by 7598

Abstract

Streamflow prediction in ungauged watersheds remains a critical challenge in hydrological science due to the absence of in situ measurements, particularly in remote, data-scarce, and developing regions. This review synthesizes recent advancements in artificial intelligence (AI) for streamflow modeling, focusing on machine learning (ML), deep learning (DL), and hybrid modeling frameworks. Three core methodological domains are examined: regionalization techniques that transfer models from gauged to ungauged basins using physiographic similarity and transfer learning; synthetic data generation through proxy variables such as NDVI, soil moisture, and digital elevation models; and model performance evaluation using both deterministic and probabilistic metrics. Findings from recent literature consistently demonstrate that AI-based models, especially Long Short-Term Memory (LSTM) networks and hybrid attention-based architectures, outperform traditional conceptual and physically based models in capturing nonlinear hydrological responses across diverse climatic and physiographic settings. The integration of AI with remote sensing enhances generalizability, particularly in ungauged and human-impacted basins. This review also addresses several persistent research gaps, including inconsistencies in model evaluation protocols, limited transferability across heterogeneous regions, a lack of reproducibility and open-source tools, and insufficient integration of physical hydrological knowledge into AI models. To bridge these gaps, future research should prioritize the development of physics-informed AI frameworks, standardized benchmarking datasets, uncertainty quantification methods, and interpretable modeling tools to support robust, scalable, and operational streamflow forecasting in ungauged watersheds. Full article

(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences)

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