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Application of Machine Learning in Hydrologic Sciences, 2nd Edition
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Application of Machine Learning in Hydrologic Sciences, 2nd Edition

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 2026 | Viewed by 1290

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 recent 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, 2nd Edition” 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

Published Papers (2 papers)

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Research

23 pages, 28331 KB  
Article
Physics-Coupled and Message-Transferred Inverse Modeling for Subsurface Flow with Very Sparse Supervision
by Haibo Cheng, Jiahao Qiao, Xian’e Xiong, Xiaodi Zhang and Wenke Wang
Water 2026, 18(10), 1205; https://doi.org/10.3390/w18101205 - 16 May 2026
Viewed by 187
Abstract
Inverse modeling for subsurface flow represents a fundamental scientific challenge in hydrogeology and geotechnical engineering, which seeks to reconstruct critical hydrogeological parameters from sparse observational constraints. The marked spatial heterogeneity of subsurface formations, combined with the prohibitively high costs of data acquisition, renders [...] Read more.
Inverse modeling for subsurface flow represents a fundamental scientific challenge in hydrogeology and geotechnical engineering, which seeks to reconstruct critical hydrogeological parameters from sparse observational constraints. The marked spatial heterogeneity of subsurface formations, combined with the prohibitively high costs of data acquisition, renders parameter inversion, especially with very sparse supervision, inherently ill-posed and susceptible to non-uniqueness and instability. Numerical simulation-based iterative inversion methods are computationally expensive and time-consuming. Purely data-driven approaches require extensive labeled data, whereas the existing physics-informed methods lack an explicit architecture-level information transfer channel between parameter and response fields. Under sparse supervision, this prevents hydraulic head observations from effectively constraining hydraulic conductivity identification, resulting in weak parameter identifiability. In this work, we propose a physics-coupled and message-transferred inverse modeling method for transient subsurface flow problems with very sparse supervision. Specifically, the static parameter field estimated by the inversion network is explicitly incorporated into the dynamic response prediction network, and the static inversion and dynamic prediction networks are physics-coupled by the governing equations in parallel. This method enables accurate hydraulic conductivity inversion under extremely limited supervision. Experiments on multiple parameter fields, label scales, and noise levels demonstrate accurate and stable inversion performance under very sparse supervision, with ensemble-based uncertainty analysis, further confirming the reliability of the proposed method. Full article
(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences, 2nd Edition)
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20 pages, 15628 KB  
Article
A Hybrid Muskingum–Machine Learning Flood Forecasting Model: Application and Evaluation in the Tarim River Basin
by Pengyang Wang, Ling Zhang, Donglin Li, Fengzhen Tang, Xin Wang and Yuanjian Wang
Water 2026, 18(9), 1077; https://doi.org/10.3390/w18091077 - 30 Apr 2026
Viewed by 659
Abstract
The traditional Muskingum model has difficulty representing complex hydraulic behaviors under high-flow conditions because it relies on simplified assumptions and fixed parameters. To address the pronounced nonlinearity and non-stationarity of flood routing in the arid Tarim River Basin, a hybrid forecasting framework was [...] Read more.
The traditional Muskingum model has difficulty representing complex hydraulic behaviors under high-flow conditions because it relies on simplified assumptions and fixed parameters. To address the pronounced nonlinearity and non-stationarity of flood routing in the arid Tarim River Basin, a hybrid forecasting framework was developed by coupling the Muskingum method with multiple machine learning algorithms (Ridge, LASSO, RF, and LSTM) to predict and correct Muskingum residuals. Global Muskingum parameters were identified using the L-BFGS-B algorithm to represent basin-scale routing characteristics. For rolling forecast, a multidimensional feature space was constructed by integrating routing gradients and hydraulic interaction terms. The results indicated that all hybrid models outperformed the traditional Muskingum method across lead times. The Ridge-based hybrid model achieved the best performance at short lead times, with the Nash–Sutcliffe efficiency (NSE) at a 4 h lead time increasing from 0.56 for the physical baseline to 0.977. For longer lead times (12–24 h), the LASSO-based hybrid model demonstrated higher robustness, which was attributed to L1-regularization-based feature selection. The key scientific contribution of this work lies in proposing a lead-time-dependent adaptive modeling strategy, revealing the structural characteristics of the residuals of the Muskingum model, and demonstrating that, in the study basin, simple linear models outperform complex models in multi-step correction. Overall, the proposed framework alleviates systematic underestimation during high-flow periods and provides a predictive scheme for arid-region rivers that preserves physical interpretability while improving forecasting accuracy. Full article
(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences, 2nd Edition)
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