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Advances in AI, Numerical, and Experimental Approaches for Water Resources Applications

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".

Deadline for manuscript submissions: 26 January 2026 | Viewed by 902

Special Issue Editors


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Guest Editor
Department of Environmental Engineering, University of Calabria, 87036 Rende, Italy
Interests: sustainable water management; drinking water risk; water supply systems; water pollution; groundwater hydrology and protection of groundwater; coastal dynamics; rehabilitation and remediation of coastal environments; coastal engineering
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Department of Environmental Engineering, University of Calabria, 87036 Rende, Italy
Interests: hydrology and subsurface flow; contaminant transport in heterogeneous porous media; soil–water interactions; environmental sustainability and natural hazards; experimental and numerical modeling of flow and solute dynamics; nature-based solutions for environmental management; machine learning for water resources

Special Issue Information

Dear Colleagues,

Recent breakthroughs in remote sensing, artificial intelligence (AI), numerical modelling, and advanced experimental techniques are reshaping the investigation and management of surface– and subsurface–water systems. This Special Issue aims to collect high-quality contributions that couple hydraulics and hydrodynamics with digital and experimental innovations to tackle current and future water resource challenges.

We welcome original research papers, comprehensive reviews, and well-documented case studies that integrate one or more of the following themes:

  • The use of AI and machine-learning algorithms for prediction, monitoring and decision support in water resource applications;
  • The use of coupled numerical–experimental approaches for groundwater, the vadose zone, rivers, estuaries, coasts and urban water networks;
  • The performance of laboratory, field or remote sensing experiments supporting fundamental or applied water science;
  • The development of nature-based or hybrid solutions that enhance sustainability and resilience under climatic and anthropogenic pressures;
  • Reproducible data infrastructures, open-data repositories, and workflows for water resource research and practice;
  • Numerical modelling of flow, transport and reactive processes across scales.

We look forward to your submissions that advance the current understanding of water resource science and engineering.

Prof. Dr. Mario Maiolo
Guest Editor

Dr. Guglielmo Federico Antonio Brunetti
Guest Editor Assistant

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
  • numerical modelling
  • remote sensing
  • experimental hydraulics
  • nature-based solutions
  • coastal risk mitigation
  • water distribution systems
  • climate change adaptation
  • hydrology
  • water resource applications

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Published Papers (1 paper)

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Research

22 pages, 2718 KB  
Article
Prediction of Time Variation of Local Scour Depth at Bridge Abutments: Comparative Analysis of Machine Learning
by Yusuf Uzun and Şerife Yurdagül Kumcu
Water 2025, 17(17), 2657; https://doi.org/10.3390/w17172657 - 8 Sep 2025
Viewed by 566
Abstract
Computing the temporal variation in clearwater scour depth around abutments is important for bridge foundation design. To reach the equilibrium scour depth at bridge abutments takes a very long time. However, the corresponding times under prototype conditions can yield values significantly greater than [...] Read more.
Computing the temporal variation in clearwater scour depth around abutments is important for bridge foundation design. To reach the equilibrium scour depth at bridge abutments takes a very long time. However, the corresponding times under prototype conditions can yield values significantly greater than the time to reach the design flood peak. Therefore, estimating the temporal variation in scour depth is necessary. This study evaluates multiple machine learning (ML) models to identify the most accurate method for predicting scour depth (Ds) over time using experimental data. The dataset of 3275 records, including flow depth (Y), abutment length (L), channel width (B), velocity (V), time (t), sediment size (d50), and Ds, was used to train and test Linear Regression (LR), Random Forest Regressor (RFR), Support Vector Regression (SVR), Gradient Boosting (GBR), XGBoost, LightGBM, and KNN models. Results demonstrated the superior performance of AI-based models over conventional regression. The RFR model achieved the highest accuracy (R2 = 0.9956, Accuracy = 99.73%), followed by KNN and GBR. In contrast, the conventional LR model performed poorly (R2 = 0.4547, Accuracy = 57.39%). This study confirms the significant potential of ML, particularly ensemble methods, to provide highly reliable scour predictions, offering a robust tool for enhancing bridge design and safety. Full article
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