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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 July 2026 | Viewed by 7600

Special Issue Editors

Prof. Dr. Mario Maiolo

E-Mail Website
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
Dr. Guglielmo Federico Antonio Brunetti

E-Mail Website
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 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

  • 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 (6 papers)

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16 pages, 2123 KB  
Article
Shallow Water and Sediment Transport with Kelvin–Voigt Seabed: Numerical Insights from Theoretical Case Studies
by Maria Antonietta Scarcella
Water 2026, 18(5), 528; https://doi.org/10.3390/w18050528 - 24 Feb 2026
Viewed by 474
Abstract
Coastal erosion is increasingly influenced by anthropogenic alterations to the sediment cycle and morphological transformations. Traditional shallow water models often neglect the mechanical behavior of the seabed and its rheological response to hydrodynamic forcing, limiting their accuracy in forecasting erosion patterns. To address [...] Read more.
Coastal erosion is increasingly influenced by anthropogenic alterations to the sediment cycle and morphological transformations. Traditional shallow water models often neglect the mechanical behavior of the seabed and its rheological response to hydrodynamic forcing, limiting their accuracy in forecasting erosion patterns. To address these limitations, this study extends the classical one-dimensional Saint-Venant (shallow water) model by incorporating effects of viscosity, frictional effects, sediment transport and viscoelasticity. The seabed is treated as a Kelvin–Voigt material, characterized by an elastic modulus and a viscous damping coefficient, to account for both immediate and time-dependent mechanical responses. Using the COMSOL Multiphysics platform, the evolution of the water column and seabed was simulated in six idealized case studies under various conditions, including changes in seabed topography and different frictional and dispersive regimes. The results demonstrate the influence of seabed topography, friction Sf, diffusion/dispersion regularization term E, and viscoelastic properties on wave seabed interactions and morphodynamic bed evolution (Exner-type). The inclusion of viscoelastic damping contributes to the stabilization of morphological evolution, mitigating abrupt changes in bathymetry and enhancing the physical realism of the simulations. The whole research aims to improve the prediction capabilities of erosion processes and advance the current modeling tools. Full article
(This article belongs to the Special Issue Advances in AI, Numerical, and Experimental Approaches for Water Resources Applications)
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23 pages, 7300 KB  
Article
Advancing Hydrological Prediction with Hybrid Quantum Neural Networks: A Comparative Study for Mile Mughan Dam
by Erfan Abdi, Mohammad Taghi Sattari, Saeed Samadianfard and Sajjad Ahmad
Water 2025, 17(24), 3592; https://doi.org/10.3390/w17243592 - 18 Dec 2025
Cited by 3 | Viewed by 1142
Abstract
Predicting dam inflow is critical for human life safety, water resource management, and hydroelectric power generation. While machine learning (ML) models address complex, nonlinear hydrological problems, quantum machine learning (QML) offers greater potential to overcome classical computational limits. This study compares a hybrid [...] Read more.
Predicting dam inflow is critical for human life safety, water resource management, and hydroelectric power generation. While machine learning (ML) models address complex, nonlinear hydrological problems, quantum machine learning (QML) offers greater potential to overcome classical computational limits. This study compares a hybrid quantum neural network (HQNN) with the following two classical models: bidirectional CNN-LSTM and support vector regression (SVR). These models were evaluated to predict monthly inflow to the Mile Mughan Dam, a transboundary hydroelectric and irrigation dam located on the Aras River between Azerbaijan and Iran, using a 14-year dataset (2010–2023) under two scenarios. In total, 70% of data was used for training and 30% for testing. The first scenario encompassed meteorological variables plus three months of inflow lags, and the second included inflow lags only. Model performance was assessed using Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Percentage Error (MAPE), and graphical plots. HQNN showed superior performance across all metrics. In Scenario 1, HQNN achieved R2 = 0.915, RMSE = 37.318 MCM, NSE = 0.908, MAPE = 8.343%; CNN-BiLSTM had R2 = 0.867, RMSE = 46.506 MCM, NSE = 0.858, MAPE = 10.795%; SVR had R2 = 0.846, RMSE = 52.372 MCM, NSE = 0.821, MAPE = 12.772%. In Scenario 2, HQNN maintained strong performance (R2 = 0.855, RMSE = 48.56 MCM, NSE = 0.845, MAPE = 9.979%) and outperformed CNN-BiLSTM (R2 = 0.810, RMSE = 56.126 MCM, NSE = 0.793, MAPE = 11.456%) and SVR (R2 = 0.801, RMSE = 60.336 MCM, NSE = 0.761, MAPE = 12.901%). In Scenario 1 and Scenario 2, HQNN increased the prediction accuracy by 19.76% and 13.47%, respectively, compared to the CNN-BiLSTM model. These results confirm HQNN’s reliability in both multivariate and univariate modeling. Full article
(This article belongs to the Special Issue Advances in AI, Numerical, and Experimental Approaches for Water Resources Applications)
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20 pages, 16148 KB  
Article
A Dual-Branch Coupled Fourier Neural Operator for High-Resolution Multi-Phase Flow Modeling in Porous Media
by Hassan Al Hashim, Odai Elyas and John Williams
Water 2025, 17(23), 3351; https://doi.org/10.3390/w17233351 - 23 Nov 2025
Cited by 1 | Viewed by 1910
Abstract
This paper investigates a physics-informed surrogate modeling framework for multi-phase flow in porous media based on the Fourier Neural Operator. Traditional numerical simulators, though accurate, suffer from severe computational bottlenecks due to fine-grid discretizations and the iterative solution of highly nonlinear partial differential [...] Read more.
This paper investigates a physics-informed surrogate modeling framework for multi-phase flow in porous media based on the Fourier Neural Operator. Traditional numerical simulators, though accurate, suffer from severe computational bottlenecks due to fine-grid discretizations and the iterative solution of highly nonlinear partial differential equations. By parameterizing the kernel integral directly in Fourier space, the operator provides a discretization-invariant mapping between function spaces, enabling efficient spectral convolutions. We introduce a Dual-Branch Adaptive Fourier Neural Operator with a shared Fourier encoder and two decoders: a saturation branch that uses an inverse Fourier transform followed by a multilayer perceptron and a pressure branch that uses a convolutional decoder. Temporal information is injected via Time2Vec embeddings and a causal temporal transformer, conditioning each forward pass on step index and time step to maintain consistent dynamics across horizons. Physics-informed losses couple data fidelity with residuals from mass conservation and Darcy pressure, enforcing the governing constraints in Fourier space; truncated spectral kernels promote generalization across meshes without retraining. On SPE10-style heterogeneities, the model shifts the infinity-norm error mass into the 10−2 to 10−1 band during early transients and sustains lower errors during pseudo-steady state. In zero-shot three-dimensional coarse-to-fine upscaling from 30×110×5 to 60×220×5, it attains R2=0.90, RMSE = 4.4×10−2, and MAE = 3.2×10−2, with more than 90% of voxels below five percent absolute error across five unseen layers, while the end-to-end pipeline runs about three times faster than a full-order fine-grid solve and preserves water-flood fronts and channel connectivity. Benchmarking against established baselines indicates a scalable, high-fidelity alternative for high-resolution multi-phase flow simulation in porous media. Full article
(This article belongs to the Special Issue Advances in AI, Numerical, and Experimental Approaches for Water Resources Applications)
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20 pages, 4476 KB  
Article
Effects of Permeability and Pyrite Distribution Heterogeneity on Pyrite Oxidation in Flooded Lignite Mine Dumps
by Tobias Schnepper, Michael Kühn and Thomas Kempka
Water 2025, 17(21), 3157; https://doi.org/10.3390/w17213157 - 4 Nov 2025
Cited by 1 | Viewed by 1055
Abstract
The role of sedimentary heterogeneity in reactive transport processes is becoming increasingly important as closed open-pit lignite mines are converted into post-mining lakes or pumped hydropower storage reservoirs. Flooding of the open pits introduces constant oxygen-rich inflows that reactivate pyrite oxidation within internal [...] Read more.
The role of sedimentary heterogeneity in reactive transport processes is becoming increasingly important as closed open-pit lignite mines are converted into post-mining lakes or pumped hydropower storage reservoirs. Flooding of the open pits introduces constant oxygen-rich inflows that reactivate pyrite oxidation within internal mine dumps. A reactive transport model coupling groundwater flow, advection–diffusion–dispersion, and geochemical reactions was applied to a 2D cross-section of a water-saturated mine dump to determine the processes governing pyrite oxidation. Spatially correlated fields representing permeability and pyrite distributions were generated via exponential covariance models reflecting the end-dumping depositional architecture, supported by a suite of scenarios with systematically varied correlation lengths and variances. Simulation results covering a time span of 100 years quantify the impact of heterogeneous permeability fields that result in preferential flow paths, which advance tracer breakthrough by ~15 % and increase the cumulative solute outflux up to 139 % relative to the homogeneous baseline. Low initial pyrite concentrations (0.05 wt %) allow for deeper oxygen penetration, extending oxidation fronts over the complete length of the modeling domain. Here, high initial pyrite concentrations (0.5 wt %) confine reactions close to the inlet. Kinetic oxidation allows for more precise simulation of redox dynamics, while equilibrium assumptions substantially reduce the computational time (>10×), but may oversimplify the redox system. We conclude that reliable risk assessments for post-mining redevelopment should not simplify numerical models by assuming average homogeneous porosity and mineral distributions, but have to incorporate site-specific spatial heterogeneity, as it critically controls acid generation, sulfate mobilization, and the timing of contaminant release. Full article
(This article belongs to the Special Issue Advances in AI, Numerical, and Experimental Approaches for Water Resources Applications)
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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
Cited by 4 | Viewed by 1568
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
(This article belongs to the Special Issue Advances in AI, Numerical, and Experimental Approaches for Water Resources Applications)
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26 pages, 2810 KB  
Systematic Review
A Systematic Review of Flood Management Evolution, with Emphasis on How Generative AI Reshapes Prediction-to-Decision Pathways
by Nadir Murtaza, Aïssa Rezzoug, Muhammad Ali Sikandar and Sohail Iqbal
Water 2026, 18(5), 582; https://doi.org/10.3390/w18050582 - 28 Feb 2026
Viewed by 689
Abstract
Climate change affects flood frequency and intensity throughout the world, leading to a research gap in the traditional management framework. Furthermore, traditional frameworks often rely on complex hydrological patterns and one-way communication, demonstrating urgent needs for adaptive and two-way communication approaches. For this [...] Read more.
Climate change affects flood frequency and intensity throughout the world, leading to a research gap in the traditional management framework. Furthermore, traditional frameworks often rely on complex hydrological patterns and one-way communication, demonstrating urgent needs for adaptive and two-way communication approaches. For this purpose, the current systematic literature review (SLR) fills this gap by analyzing the widely reported literature on the role of an artificial intelligence (AI)-based framework. This SLR provides conceptual and theoretical insight into the potential role of generative AI and an OpenAI-based theoretical framework for effective flood management. Therefore, 77 peer-reviewed articles published between 2010 and 2025 in reputed sources such as ScienceDirect, Springer Nature, MDPI, Wiley, and others were analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. According to the results of this paper, four hypothetical applications of generative AI are described, namely: (i) a knowledge translator to provide simplified hydrological information, (ii) a decision-support assistant that aids real-time strategic analysis, (iii) a community engagement tool to increase the participation and understanding of people, and (iv) an interface to harmonize and synthesize various sources of information. The discussion indicates that there is a lot of potential in terms of generative AI improving the inclusiveness, real-time sensitivity, and cost-effectiveness of flood risk management practice. Nevertheless, the research also presents significant issues that are connected to data integrity, algorithm bias, digital equity, and ethical governance. The results indicate that generative AI has a significant potential of developing robust, more accessible, and more communicative flood risk management systems, and that additional studies on the responsible and ethical use of the technology are necessary. Full article
(This article belongs to the Special Issue Advances in AI, Numerical, and Experimental Approaches for Water Resources Applications)
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