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Water-Induced Geological Hazard Risk Assessment: Recent Advances and Prospects
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Water-Induced Geological Hazard Risk Assessment: Recent Advances and Prospects

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

Deadline for manuscript submissions: 20 August 2025 | Viewed by 5056

Special Issue Editor

Prof. Dr. Gang Mei

E-Mail Website
Guest Editor
School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Interests: water-related disaster investigation; engineering geology; computational modeling; remote sensing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water-induced geological hazards, including landslides, debris flows, and rock avalanches, pose a major threat to human life, property, and natural ecosystems. These risks are exacerbated by the increasing frequency and intensity of extreme weather events driven by global climate change. This Special Issue aims to explore recent advances in assessing, predicting, and mitigating water-induced geohazards using multidisciplinary approaches such as artificial intelligence, numerical modeling, field investigations, and remote sensing techniques.

We invite contributions from scientists and researchers exploring innovative methods and techniques to assess, predict, and mitigate water-induced geohazards. Contributions may include original research articles, case studies, reviews, and theoretical papers addressing the mechanisms, monitoring, modeling, and risk management of these hazards.

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

  • Mechanisms of water-induced landslides and slope failures;
  • Numerical modeling and simulation of geohazard processes;
  • Risk assessment and mitigation strategies for water-induced geohazards;
  • Field and laboratory investigations of hydrological and geological processes;
  • Remote sensing and GIS-based water-induced geohazards mapping and monitoring;
  • Case studies on water-induced geological hazards and their impacts;
  • Application of artificial intelligence for water-induced geohazards forecasting;
  • Deep learning for water-induced geohazards detection.

Prof. Dr. Gang Mei
Guest Editor

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

  • water-induced geological hazards
  • landslides
  • debris flows
  • rock avalanches
  • artificial intelligence
  • numerical modeling
  • remote sensing
  • GIS analysis
  • geological hazard mapping
  • climate change impacts

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Published Papers (4 papers)

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Research

18 pages, 7864 KiB  
Article
Impact Response Characteristics of Apron Structure to Bouldery Debris Flow
by Shihua Chen, Minghui Meng, Tingting Jiang, Jian Guo, Dong Sun, Liang Qin and Tiantao Li
Water 2025, 17(4), 544; https://doi.org/10.3390/w17040544 - 13 Feb 2025
Viewed by 700
Abstract
Apron is a commonly used structure in the downstream of debris-flow-retaining dams. Its function is to resist the impact and erosion of debris flow on the dam foundation. In order to enhance the impact resistance of the apron to boulders, increasing the apron [...] Read more.
Apron is a commonly used structure in the downstream of debris-flow-retaining dams. Its function is to resist the impact and erosion of debris flow on the dam foundation. In order to enhance the impact resistance of the apron to boulders, increasing the apron thickness and filling the block stone are usually adopted. However, the apron is still often destroyed by bouldery debris flow. Therefore, we propose a kind of toughness apron. Physical test and numerical simulation are used to reveal the dynamic response of the toughness apron. The results show that both tire cushion and stone cushion can buffer the impact of boulders. The physical test showed that the cushion reduces impact force and vibration acceleration, and the numerical simulation results indicate that the cushion significantly reduces damage to the protection apron while dissipating most of the energy. It was also found that there is an energy threshold of impact damage resistance of the apron. When the impact kinetic energy is higher than this threshold, the apron will be damaged. These findings highlight its potential for debris flow protection. According to the corresponding impact characteristics of the dam, the design method of the toughness apron is proposed. Full article
(This article belongs to the Special Issue Water-Induced Geological Hazard Risk Assessment: Recent Advances and Prospects)
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18 pages, 2660 KiB  
Article
A Hybrid Approach to Mountain Torrent-Induced Debris Flow Prediction Combining Experiments and Gradient Boosting Regression
by Hanze Li, Xinhai Zhang, Yazhou Fan, Shijie Peng, Lu Zhang, Dabo Xiang, Jingjing Liao, Jinxin Zhang and Zhenzhu Meng
Water 2024, 16(23), 3519; https://doi.org/10.3390/w16233519 - 6 Dec 2024
Viewed by 1229
Abstract
Debris flows are highly unpredictable and destructive natural hazards that present significant risks to both human life and infrastructure. Despite advances in machine learning techniques, current predictive models often fall short due to the insufficient and low-quality historical data available for training. In [...] Read more.
Debris flows are highly unpredictable and destructive natural hazards that present significant risks to both human life and infrastructure. Despite advances in machine learning techniques, current predictive models often fall short due to the insufficient and low-quality historical data available for training. In this study, we introduce a hybrid approach that combines physical model experiments with a gradient boosting regression model to enhance the accuracy and reliability of debris flow predictions. By integrating experimental data that closely simulate real-world flow conditions, the gradient boosting regression model is trained on a more robust foundation, enabling it to capture the complex dynamics of debris flows under various conditions. Selecting slide mass, slope length, yield stress, and slope angle as explanatory variables, we focus on quantify two critical debris flow parameters—frontal thickness and velocity—at indicated locations within the flow. The model demonstrates strong predictive performance in forecasting these key parameters, achieving coefficients of determination of 0.938 for slide thickness and 0.934 for slide velocity. This hybrid approach not only simplifies the prediction process but also significantly improves its precision, offering a valuable tool for real-time risk assessment and mitigation strategies in debris flow-prone regions. Full article
(This article belongs to the Special Issue Water-Induced Geological Hazard Risk Assessment: Recent Advances and Prospects)
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19 pages, 3912 KiB  
Article
Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China
by Yu Feng, Yaoshan Bi and Dong Li
Water 2024, 16(22), 3262; https://doi.org/10.3390/w16223262 - 13 Nov 2024
Viewed by 888
Abstract
Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam [...] Read more.
Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines. Full article
(This article belongs to the Special Issue Water-Induced Geological Hazard Risk Assessment: Recent Advances and Prospects)
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21 pages, 21852 KiB  
Article
A Case Study for Analysis of Stability and Treatment Measures of a Landslide Under Rainfall with the Changes in Pore Water Pressure
by Liangzhi Tang, Yun Yan, Faming Zhang, Xiaokai Li, Yuhong Liang, Yuru Yan, Huaqing Zhang and Xiaolong Zhang
Water 2024, 16(21), 3113; https://doi.org/10.3390/w16213113 - 30 Oct 2024
Cited by 5 | Viewed by 1601
Abstract
Mining causes damage to the soil and rock mass, while rainfall has a pivotal impact on the mining slope stability, even leading to geological hazards such as landslides. Therefore, the study evaluated the mine landslide stability and determined the effectiveness of the treatment [...] Read more.
Mining causes damage to the soil and rock mass, while rainfall has a pivotal impact on the mining slope stability, even leading to geological hazards such as landslides. Therefore, the study evaluated the mine landslide stability and determined the effectiveness of the treatment measures under the impact of pore water pressure changes caused by rainfall, taking the Kong Mountain landslide in Nanjing, Jiangsu Province, China, as the research object. The geological conditions and deformation characteristics were clarified, and the failure mechanism and influencing factors were analyzed. Also, the landslide stability was comprehensively evaluated and calculated utilizing the finite element-improved limit equilibrium method and FLAC 3D 6.0, which simulated the distribution of pore water pressure, displacement, etc., to analyze the influence of rainfall conditions and reinforcement effects. The results indicated the following: (1) Rainfall is the key influencing factor of the landslide stability, which caused the pore water pressure changes and the loosening of the soil due to the strong permeability; (2) The distribution of the pore water pressure and plastic zone showed that, during the rainfall process, a large area of transient saturation zone appeared at the leading edge, affecting the stability of the whole landslide and led to the further deformation; (3) After the application of treatment measures (anti-sliding piles and anchor cables), the landslide stability increased under both natural and rainfall conditions (from 1.02 and 0.94 to 1.38 and 1.31, respectively), along with a reduction in displacement, plastic zones, etc. The Kong Mountain landslide, with the implemented treatment measures, is in good stability, which is in line with the evaluation and calculation results. The study provides certain contributions to the stability evaluation and treatment selection of similar engineering under rainfall infiltration. Full article
(This article belongs to the Special Issue Water-Induced Geological Hazard Risk Assessment: Recent Advances and Prospects)
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