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Intelligent Analysis, Monitoring and Assessment of Debris Flow

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

Deadline for manuscript submissions: closed (20 January 2026) | Viewed by 6113

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Special Issue Editor

Prof. Dr. Weijie Zhang

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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
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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:

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

We look forward to receiving your contributions.

Prof. Dr. Weijie Zhang
Guest Editor

Manuscript Submission Information

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Keywords

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

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Published Papers (7 papers)

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Research

19 pages, 3626 KB

Open AccessArticle

Stability Analysis of High-Fill Slopes with EPS–Spoil Composite in Gullies Under Rainfall Conditions: From Scheme to Practice

by Yijun Xiu and Fei Ye

Water 2026, 18(8), 921; https://doi.org/10.3390/w18080921 - 13 Apr 2026

Viewed by 425

Abstract

Utilizing excavated waste soil to level gullies offers significant advantages in terms of engineering economy and construction efficiency. However, the stability and deformation risks of high-fill embankments in mountainous gullies under rainfall conditions have attracted significant attention, particularly when such structures are located adjacent to residential areas. This study compares two design schemes for highway high-fill embankments, Scheme 1: high-fill slope supported by stabilizing piles and prestressed anchors, and Scheme 2: ordinary waste soil as the core, foamed lightweight soil (EPS) as the edge band, and reinforcement by a micro-pile retaining wall system. Finite element analysis was used to evaluate the Factor of Safety (FOS), displacements of retaining structures, and characteristic slope points under three conditions (no rainfall, heavy rainfall, and heavy rainfall with soil strength deterioration). The results show that Scheme 2 reduces total costs by 3.5%, shortens the construction period by 14%, and cuts maintenance costs by 65%, with a minimum FOS of 1.56 under extreme rainfall. Further parametric analysis of Scheme 2 optimized key design parameters, and field monitoring data over 6 months verified the reliability of the numerical simulation. This study provides a transferable design-verification pathway for combining lightweight and conventional fills in high embankments, offering technical support for similar projects in complex mountainous areas. Full article

(This article belongs to the Special Issue Intelligent Analysis, Monitoring and Assessment of Debris Flow)

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15 pages, 10069 KB

Open AccessArticle

Hazard Assessment for Potential GLOF of JiongpuCo Glacial Lake, Southeastern Tibet

by Na He, Xuan Liu, Hao Wang, Weiming Liu, Miaohui Zhang, Jingxuan Cao and Yang Yang

Water 2026, 18(5), 628; https://doi.org/10.3390/w18050628 - 6 Mar 2026

Viewed by 418

Abstract

This study examined the glacial lake of JiongpuCo in the southeastern Tibet region. According to satellite images obtained by Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) from 1995 to 2025, JiongpuCo’s area expanded from 1.92 ± 0.06 km² to 5.26 ± 0.02 km², which is a 174% increase over 30 years. The lake was in a state of dynamic equilibrium. The bathymetric data showed that JiongpuCo has a basin-like morphology. Its reservoir capacity curve was concave-up, with a maximum water depth of 237 m and total reservoir capacity of 6.35 × 10⁸ m³. A sequential HEC-RAS-MIKE 21 numerical modeling framework was constructed to simulate flood propagation. For three simulated scenarios (with breach volumes of 80%, 60%, and 30%), the peak discharge at the breach outlet was 28,368.45 m³/s, 25,451.67 m³/s, and 17,855.54 m³/s. Analysis of the simulation results shows that the glacier lake outburst flood (GLOF) has continuous attenuation of peak discharge and a gradual lag in arrival time along the flow path. Except for Bagai in Scenarios 2 and 3, all other target research towns and villages were flooded by floodwaters. These findings offer a solid scientific foundation for the reduction in GLOF disasters and the development of an early warning system for JiongpuCo. Full article

(This article belongs to the Special Issue Intelligent Analysis, Monitoring and Assessment of Debris Flow)

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17 pages, 928 KB

Open AccessArticle

Dynamic Threshold Determination Method for Triggering Critical Rainfall in Mountainous Debris Flow

by Yixian Wang and Na He

Water 2026, 18(4), 484; https://doi.org/10.3390/w18040484 - 13 Feb 2026

Viewed by 416

Abstract

The initiation of debris flows in mountainous areas is dynamically influenced by multiple factors, including rainfall intensity, duration, and antecedent rainfall conditions. Traditional static threshold methods struggle to adapt to these dynamic environmental conditions. To address this issue, this paper proposes a dynamic threshold determination method for the critical rainfall triggering debris flows in mountainous regions. Firstly, high-risk areas are identified based on the frequency ratio model, and the effective rainfall is quantified using the Crozier model. Subsequently, a combination of dynamic variables, such as soil saturation and safety factor, is constructed, and the Jensen–Shannon (JS) divergence is introduced for sensitivity screening to select the most relevant variables. These optimized variables are then fed into an LSTM-TCN (Long Short-Term Memory-Temporal Convolutional Network) framework to extract temporal features and predict the probability of debris flow occurrence time. Finally, real-time threshold determination is achieved by integrating the absolute rainfall energy with a dynamic threshold model. Test results demonstrate that this method can effectively quantify the dynamic nature of rainfall across different regions, screen key variables, and achieve threshold determination with high coverage (average of 0.978) and precise interval width (average of 0.023). This approach provides a more accurate and adaptive means of predicting and managing debris flow risks in mountainous areas, enhancing our ability to respond to these natural hazards in a timely and effective manner. Full article

(This article belongs to the Special Issue Intelligent Analysis, Monitoring and Assessment of Debris Flow)

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14 pages, 2398 KB

Open AccessArticle

Intelligent Assessment of Landslide Impact Force Considering the Uncertainty of Strength Parameters

by Xinyi Hong, Weijie Zhang, Xin Wang, Hongxin Chen and Yongqi Xue

Water 2025, 17(24), 3595; https://doi.org/10.3390/w17243595 - 18 Dec 2025

Cited by 1 | Viewed by 550

Abstract

Accurately predicting the peak impact force exerted by landslides on bridge piers is crucial for evaluating structural safety. However, the reliability of such predictions is frequently undermined by the spatial variability and uncertainty inherent in soil and rock strength parameters. To quantify the influence of this uncertainty, in this study, a three-dimensional numerical model of a landslide impacting bridge piers was developed using LS-DYNA software (version R11.0.0). A neural network was then trained on the peak impact forces simulated by the numerical model. Based on the neural network predictions, the impact mechanisms were categorized into two distinct modes, namely, a low-impact mode and a high-impact mode, for a comparative analysis. The results revealed statistically significant differences in soil parameters between these modes. Specifically, low-impact forces (F < 467 kN) were found to correlate with higher cohesion (18.5–24.9 kPa) and lower internal friction angles (15–22.4°). Conversely, high-impact forces (F ≥ 467 kN) were associated with lower cohesion (14.0–21.6 kPa) and higher internal friction angles (18.1–25.3°). This negative correlation highlights the decisive role that the combined uncertainty of strength parameters plays in predicting the peak impact force. Moreover, the surrogate model developed in this study effectively addresses the computational inefficiencies commonly associated with Monte Carlo simulations. This methodology provides a valuable tool for evaluating the vulnerability of infrastructure systems exposed to landslide hazards. Full article

(This article belongs to the Special Issue Intelligent Analysis, Monitoring and Assessment of Debris Flow)

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26 pages, 6361 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1322

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1264

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1027

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 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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