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Water
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Application of Machine Learning in Hydrologic Sciences
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Application of Machine Learning in Hydrologic Sciences

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: 20 August 2025 | Viewed by 2726

Special Issue Editors

Dr. Gonzalo Astray

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Diego Fernández-Nóvoa

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

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 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

  • 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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Published Papers (2 papers)

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Research

29 pages, 5371 KiB  
Article
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
Viewed by 596
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 [...] Read more.
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 KiB  
Article
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 1 | Viewed by 1253
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 [...] Read more.
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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