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Intelligent Analysis, Monitoring and Assessment of Debris Flow
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Intelligent Analysis, Monitoring and Assessment of Debris Flow

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

Deadline for manuscript submissions: 20 January 2026 | Viewed by 2235

Special Issue Editor

Prof. Dr. Weijie Zhang

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Hohai University, Nanjing, China
Interests: extreme rainfall; water–soil coupling; debris flow; numerical simulation; impact force; machine learning; artificial neural network; risk assessment; mitigation measures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Extreme rainfall events occur frequently around the world and trigger a number of geohazards, causing great suffering and loss. Debris flow, one such serious water-induced geohazard, has attracted more and more attention from scholars worldwide who seek to prevent disasters and reduce damage. Meanwhile, artificial intelligence techniques have developed substantially, providing new analysis approaches, monitoring means, and risk evaluation tools for debris flows. Related studies can enhance our understanding of this kind of hazard and assist in hazard mitigation.

Therefore, this Special Issue aims to present original research and review articles that discuss slope stability under rainfall, water–soil coupling mechanisms in the flow process, and site monitoring and hazard assessment of debris flows using artificial intelligence.

Potential topics include, but are not limited to, the following:

  1. Stability analysis of slopes under rainfall events;
  2. Water–soil coupling in the flow process;
  3. Artificial intelligence approaches in the analysis of debris flows;
  4. Site monitoring of potential debris flows;
  5. Influence range evaluation and risk assessment.

We look forward to receiving your contributions.

Prof. Dr. Weijie Zhang
Guest Editor

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

  • debris flow
  • water–soil coupling
  • artificial intelligence
  • numerical analysis
  • monitoring
  • risk evaluation
  • mitigation measure

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Published Papers (3 papers)

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Research

26 pages, 6361 KB  
Article
Improving the Generalization Performance of Debris-Flow Susceptibility Modeling by a Stacking Ensemble Learning-Based Negative Sample Strategy
by Jiayi Li, Jialan Zhang, Jingyuan Yu, Yongbo Chu and Haijia Wen
Water 2025, 17(16), 2460; https://doi.org/10.3390/w17162460 - 19 Aug 2025
Viewed by 680
Abstract
To address the negative sample selection bias and limited interpretability of traditional debris-flow event susceptibility models, this study proposes a framework that enhances generalization by integrating negative sample screening via a stacking ensemble model with an interpretable random forest. Using Wenchuan County, Sichuan [...] Read more.
To address the negative sample selection bias and limited interpretability of traditional debris-flow event susceptibility models, this study proposes a framework that enhances generalization by integrating negative sample screening via a stacking ensemble model with an interpretable random forest. Using Wenchuan County, Sichuan Province, as the study area, 19 influencing factors were selected, encompassing topographic, geological, environmental, and anthropogenic variables. First, a stacking ensemble—comprising logistic regression (LR), decision tree (DT), gradient boosting decision tree (GBDT), and random forest (RF)—was employed as a preliminary classifier to identify very low-susceptibility areas as reliable negative samples, achieving a balanced 1:1 ratio of positive to negative instances. Subsequently, a stacking–random forest model (Stacking-RF) was trained for susceptibility zonation, and SHAP (Shapley additive explanations) was applied to quantify each factor’s contribution. The results show that: (1) the stacking ensemble achieved a test-set AUC (area under the receiver operating characteristic curve) of 0.9044, confirming its effectiveness in screening dependable negative samples; (2) the random forest model attained a test-set AUC of 0.9931, with very high-susceptibility zones—covering 15.86% of the study area—encompassing 92.3% of historical debris-flow events; (3) SHAP analysis identified the distance to a road and point-of-interest (POI) kernel density as the primary drivers of debris-flow susceptibility. The method quantified nonlinear impact thresholds, revealing significant susceptibility increases when road distance was less than 500 m or POI kernel density ranged between 50 and 200 units/km2; and (4) cross-regional validation in Qingchuan County demonstrated that the proposed model improved the capture rate for high/very high susceptibility areas by 48.86%, improving it from 4.55% to 53.41%, with a site density of 0.0469 events/km2 in very high-susceptibility zones. Overall, this framework offers a high-precision and interpretable debris-flow risk management tool, highlights the substantial influence of anthropogenic factors such as roads and land development, and introduces a “negative-sample screening with cross-regional generalization” strategy to support land-use planning and disaster prevention in mountainous regions. Full article
(This article belongs to the Special Issue Intelligent Analysis, Monitoring and Assessment of Debris Flow)
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27 pages, 22330 KB  
Article
Optimizing Landslide Susceptibility Mapping with Non-Landslide Sampling Strategy and Spatio-Temporal Fusion Models
by Jun-Han Deng, Hui-Ying Guo, Hong-Zhi Cui and Jian Ji
Water 2025, 17(12), 1778; https://doi.org/10.3390/w17121778 - 13 Jun 2025
Viewed by 644
Abstract
Landslides are among the most destructive geological hazards, necessitating precise landslide susceptibility mapping (LSM) for effective risk management. This study focuses on the northeastern region of Leshan City and investigates the influence of various non-landslide sampling strategies and machine learning (ML) models on [...] Read more.
Landslides are among the most destructive geological hazards, necessitating precise landslide susceptibility mapping (LSM) for effective risk management. This study focuses on the northeastern region of Leshan City and investigates the influence of various non-landslide sampling strategies and machine learning (ML) models on LSM performance. Ten landslide conditioning factors, selected by SHAP analysis, and six models were utilized: Convolutional neural networks (CNNs), Long Short-Term Memory (LSTM), CNN-LSTM, CNN-LSTM with an attention mechanism (AM), Random Forest (RF), and eXtreme Gradient Boosting combined with Logistic Regression (XGBoost-LR). Three non-landslide sampling strategies were designed, with the certainty factor-based approach demonstrating superior performance by effectively capturing geological and physical characteristics, applying spatial constraints to exclude high-risk zones, and achieving improved mean squared error (MSE) and area under the curve (AUC) values. The results reveal that traditional ML models struggle with complex nonlinear relationships and imbalanced datasets, often leading to high false positive rates. In contrast, deep learning (DL) models—particularly CNN-LSTM-AM—achieved the best performance, with an AUC of 0.9044 and enhanced balance in accuracy, precision, recall, and Kappa. These improvements are attributed to the model’s ability to extract static spatial features (via CNNs), capture dynamic temporal patterns (via LSTM), and emphasize key features through the attention mechanism. This integrated architecture enhances the capacity to process heterogeneous data and extract landslide-relevant features. Overall, optimizing non-landslide sampling strategies, incorporating comprehensive geophysical information, enforcing spatial constraints, and enhancing feature extraction capabilities are essential for improving the accuracy and reliability of LSM. Full article
(This article belongs to the Special Issue Intelligent Analysis, Monitoring and Assessment of Debris Flow)
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22 pages, 3394 KB  
Article
Temporal and Spatial Analysis of Deformation and Instability, and Trend Analysis of Step Deformation Landslide
by Jiakun Wang, Rui Chen, Jing Ren, Senlin Li, Aiping Yang, Yang Zhou and Licheng Yang
Water 2025, 17(11), 1684; https://doi.org/10.3390/w17111684 - 2 Jun 2025
Viewed by 586
Abstract
This study focuses on step deformation landslides, conducting spatiotemporal analysis of landslide deformation and instability trends. First, the target landslide area is selected, and geological and precipitation data, along with historical displacement data from monitoring points, are collected. The slope single-change-point analysis method [...] Read more.
This study focuses on step deformation landslides, conducting spatiotemporal analysis of landslide deformation and instability trends. First, the target landslide area is selected, and geological and precipitation data, along with historical displacement data from monitoring points, are collected. The slope single-change-point analysis method is then employed, combined with landslide profile data, to extract key features from the monitoring data. Next, Small BAseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology is applied to obtain satellite images of the study area. These images, together with the extracted data features, are used to draw the spatiotemporal baseline of the target landslide, completing the spatiotemporal analysis. Finally, a landslide prediction model is developed, and its prediction error is corrected using an Extreme Learning Machine (ELM) neural network. The refined prediction results serve as the basis for analyzing the landslide deformation coefficient, enabling the determination of the landslide instability trend. The experimental results show that step deformation landslides exhibit significant spatiotemporal variability and a short stability period throughout the year. The analytical methods designed in this study outperform traditional methods, providing more reliable results for predicting landslide instability trends. Full article
(This article belongs to the Special Issue Intelligent Analysis, Monitoring and Assessment of Debris Flow)
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