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Advances in Machine Learning and Artificial Intelligence Technologies for Hydrological Processes and Hydrologic Disasters

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2268

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Special Issue Editors

Dr. Huaxiang Yan

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Guest Editor

Zijin School of Geology and Mining, Fuzhou University, Fuzhou, China
Interests: machine learning; groundwater transport; hydrological modelling; water infiltration; transport in porous media

Dr. Hongqiang Dou

E-Mail Website
Guest Editor

Zijin School of Geology and Mining, Fuzhou University, Fuzhou, China
Interests: geologic hazards and prevention; machine learning; risk assessment; rainfall infiltration model; reliability analysis; landslide early warning

Special Issue Information

Dear Colleagues,

The Special Issue, titled "Advances in Machine Learning and Artificial Intelligence Technologies for Hydrological Processes and Hydrologic Disasters", aims to highlight the latest developments and applications of artificial intelligence (AI) technologies and machine learning (ML) in addressing challenges related to hydrological processes and disasters. The development of machine learning and artificial intelligence technologies offers innovative approaches to understanding, modeling and predicting the movement and storage of water in the environment, as well as its interactions with geological, atmospheric and ecological systems. Traditional methods often struggle to capture the highly nonlinear and spatiotemporal variability of water systems, such as rainfall–runoff processes, unsaturated infiltration, groundwater dynamics and water-induced geologic hazards. However, recent advances in ML and AI provide alternative tools to enhance predictive capabilities, optimize resource management, and improve rise assessment capability.

We invite contributions that include original research articles, case studies, reviews, and theoretical papers from scientists and researchers exploring the integration of AI and ML technologies in hydrological contexts. We warmly welcome submissions focused on innovative methodologies or applications that tackle key challenges in understanding, predicting and managing hydrological processes and related disasters.

Topics of interest include, but are not limited to, the following:

AI-driven hydrological models;
Machine learning applications in rainfall–runoff prediction;
Data assimilation techniques;
Interpretable machine learning for hydrological systems;
Data-driven approaches for groundwater dynamics and flow modeling;
Modeling uncertainties and their effects;
Risk assessment and reduction strategies;
Assessing, predicting, and mitigating water-induced geohazards.

Dr. Huaxiang Yan
Dr. Hongqiang Dou
Guest Editors

Manuscript Submission Information

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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

artificial intelligence technologies
groundwater transport
hydrological processes
hydrologic disasters
hydrologi-cal modelling
machine learning
soil water

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Published Papers (3 papers)

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Research

18 pages, 3956 KiB

Open AccessArticle

Identification of Gully-Type Debris Flow Shapes Based on Point Cloud Local Curvature Extrema

by Ruoyu Tan and Bohan Zhang

Water 2025, 17(9), 1243; https://doi.org/10.3390/w17091243 - 22 Apr 2025

Viewed by 105

Abstract

The identification of gully-type debris flow remains a challenging task due to the irregularity of terrain, which causes significant fluctuations in local curvature and hinders accurate feature extraction using traditional methods. To address this issue, this study proposes a novel identification approach based on point cloud local curvature extrema. The methodology involves collecting image data of debris flow and landslide areas using DJI Matrice 300 RTK (M300RTK), planning control points and flight routes, and generating three-dimensional point cloud data through image matching and point cloud reconstruction techniques. A quadratic surface fitting method was employed to calculate the curvature of each point in the point cloud, while a topological k-neighborhood algorithm was introduced to establish spatial relationships and extract extreme curvature features. These features were subsequently used as inputs to a convolutional neural network (CNN) for landslide identification. Experimental results demonstrated that the CNN architecture used in this method achieved rapid convergence, with the loss value decreasing to 0.0032 (cross-entropy loss) during training, verifying the model’s effectiveness. The introduction of early stopping and learning rate decay strategies effectively prevented overfitting. Receiver-operating characteristic (ROC) curve analysis revealed that the proposed method achieved an area under the ROC curve (AUC) of 0.92, significantly outperforming comparative methods (0.78–0.85). Full article

(This article belongs to the Special Issue Advances in Machine Learning and Artificial Intelligence Technologies for Hydrological Processes and Hydrologic Disasters)

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22 pages, 4716 KiB

Open AccessArticle

Global Sensitivity Analysis of Slope Stability Considering Effective Rainfall with Analytical Solutions

by Chuan-An Xia, Jing-Quan Zhang, Hao Wang and Wen-Bin Jian

Water 2025, 17(2), 141; https://doi.org/10.3390/w17020141 - 7 Jan 2025

Cited by 1 | Viewed by 765

Abstract

Rainfall-induced landslides are widely distributed in many countries. Rainfall impacts the hydraulic dynamics of groundwater and, therefore, slope stability. We derive an analytical solution of slope stability considering effective rainfall based on the Richards equation. We define effective rainfall as the total volume of rainfall stored within a given range of the unsaturated zone during rainfall events. The slope stability at the depth of interest is provided as a function of effective rainfall. The validity of analytical solutions of system states related to effective rainfall, for infinite slopes of a granite residual soil, is verified by comparing them with the corresponding numerical solutions. Additionally, three approaches to global sensitivity analysis are used to compute the sensitivity of the slope stability to a variety of factors of interest. These factors are the reciprocal of the air-entry value of the soil α, the thickness of the unsaturated zone L, the cohesion of soil c, the internal friction angle ϕ related to the effective normal stress, the slope angle β, the unit weights of soil particles γ_s, and the saturated hydraulic conductivity K_s. The results show the following: (1) The analytical solutions are accurate in terms of the relative differences between the analytical and the numerical solutions, which are within 5.00% when considering the latter as references. (2) The temporal evolutions of the shear strength of soil can be sequentially characterized as four periods: (i) strength improvement due to the increasing weight of soil caused by rainfall infiltration, (ii) strength reduction controlled by the increasing pore water pressure, (iii) strength reduction due to the effect of hydrostatic pressure in the transient saturation zone, and (iv) stable strength when all the soil is saturated. (3) The large α corresponds to high effective rainfall. (4) The factors ranked in descending order of sensitivity are as follows: α > L > c > β > γ_s > K_s > ϕ. Full article

(This article belongs to the Special Issue Advances in Machine Learning and Artificial Intelligence Technologies for Hydrological Processes and Hydrologic Disasters)

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18 pages, 11145 KiB

Open AccessArticle

Improving Hydrological Simulations with a Dynamic Vegetation Parameter Framework

by Haiting Gu, Yutai Ke, Zhixu Bai, Di Ma, Qianwen Wu, Jiongwei Sun and Wanghua Yang

Water 2024, 16(22), 3335; https://doi.org/10.3390/w16223335 - 20 Nov 2024

Viewed by 784

Abstract

Many hydrological models incorporate vegetation-related parameters to describe hydrological processes more precisely. These parameters should adjust dynamically in response to seasonal changes in vegetation. However, due to limited information or methodological constraints, vegetation-related parameters in hydrological models are often treated as fixed values, which restricts model performance and hinders the accurate representation of hydrological responses to vegetation changes. To address this issue, a vegetation-related dynamic-parameter framework is applied on the Xinanjiang (XAJ) model, which is noted as Eco-XAJ. The dynamic-parameter framework establishes the regression between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration parameter K. Two routing methods are used in the models, i.e., the unit hydrograph (XAJ-UH and Eco-XAJ-UH) and the Linear Reservoir (XAJ-LR and Eco-XAJ-LR). The original XAJ model and the modified Eco-XAJ model are applied to the Ou River Basin, with detailed comparisons and analyses conducted under various scenarios. The results indicate that the Eco-XAJ model outperforms the original model in long-term discharge simulations, with the NSE increasing from 0.635 of XAJ-UH to 0.647 of Eco-XAJ-UH. The Eco-XAJ model also reduces overestimation and incorrect peak flow simulations during dry seasons, especially in the year 1991. In drought events, the modified model significantly enhances water balance performance. The Eco-XAJ-UH outperforms the XAJ-UH in 9 out of 16 drought events, while the Eco-XAJ-LR outperforms the XAJ-LR in 14 out of 16 drought events. The results demonstrate that the dynamic-parameter model, in regard to vegetation changes, offers more accurate simulations of hydrological processes across different scenarios, and its parameters have reasonable physical interpretations. Full article

(This article belongs to the Special Issue Advances in Machine Learning and Artificial Intelligence Technologies for Hydrological Processes and Hydrologic Disasters)

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