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Water-Related Landslide Hazard Process and Its Triggering Events—2nd Edition
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Water-Related Landslide Hazard Process and Its Triggering Events—2nd Edition

A Special Issue of Water (ISSN 2073-4441) belonging to the section "Hydrogeology".

Deadline for manuscript submissions: 20 February 2027 | Viewed by 751

Editor

Dr. Wei Liu

E-Mail Website
Guest Editor
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Interests: landslide movement; debris flow formation; dynamic mechanism; numerical simu-lation; mountain disaster chain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water plays a pivotal role in increasing the likelihood of landslide hazards through soil saturation, pressure elevation, lubrication, erosion induction, and contribution to hydraulic fracturing. Its impact on landslides encompasses various aspects, including triggering events, slope deformation, and mass movement. Understanding the interactions between water and slopes is crucial for accurately predicting and effectively mitigating landslide hazards. However, several unresolved issues persist.

This Special Issue will comprehensively explore the role of water in landslide formation, focusing on theory, methodology, and practical applications. This scope aligns with the established research domains of field investigations, remote sensing monitoring, numerical simulations, physical model experiments, and risk assessment, as well as the emerging areas of deep learning and the integration of environmental water and soil. Researchers are invited to submit their original and innovative work for potential inclusion in this Special Issue. High-quality reviews are also encouraged. Contributions that enrich our understanding of the role of water in landslide hazards are eagerly anticipated and warmly welcomed.

Dr. Wei Liu
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 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-anonymized 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

  • landslide
  • rainfall infiltration
  • pore water pressure
  • liquid–solid multiphase flow
  • hydrology
  • numerical simu-lation
  • risk assessment

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Published Papers (1 paper)

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Research

27 pages, 10203 KB  
Article
Uncertainty-Aware and Explainable Run-Out Risk Prediction of Rainfall-Induced Landslides Using a CQR-EVT-XAI Framework
by Zhenzhu Meng, Faqing Jin, Yujia Lan, Yuhong Zheng, Cheng Zeng, Le Yu, Xian Liu and Jinxin Zhang
Water 2026, 18(12), 1423; https://doi.org/10.3390/w18121423 - 10 Jun 2026
Viewed by 406
Abstract
Reliable prediction of post-initiation run-out distance of rainfall-induced landslides is essential for hazard assessment, evacuation planning, and disaster-risk mitigation. However, most existing data-driven approaches formulate run-out prediction as a deterministic regression problem and therefore provide limited information on predictive uncertainty, rare long-runout events, [...] Read more.
Reliable prediction of post-initiation run-out distance of rainfall-induced landslides is essential for hazard assessment, evacuation planning, and disaster-risk mitigation. However, most existing data-driven approaches formulate run-out prediction as a deterministic regression problem and therefore provide limited information on predictive uncertainty, rare long-runout events, and explainable decision support. To address these limitations, this study proposes CQR-EVT-XAI, a trustworthy AI framework that integrates Quantile LightGBM, Conformalized Quantile Regression (CQR), Extreme Value Theory (EVT), and Explainable Artificial Intelligence (XAI) for uncertainty-aware and explainable landslide run-out risk prediction. Based on 10,158 rainfall-induced landslide samples, physics-informed features are constructed from elevation difference H, source area A, source volume V, and mean slope angle θ. The proposed framework generates calibrated prediction intervals, threshold-based exceedance probabilities, upper-tail risk indicators, and interpretable risk levels. The CQR-LightGBM median model achieves high point-prediction accuracy, with R2 = 0.939, RMSE = 18.03 m, and MAE = 6.55 m. Conformal calibration improves the empirical coverage of the nominal 90% and 95% prediction intervals from 0.813 to 0.903 and from 0.876 to 0.953, respectively. Tail-risk analysis shows that the upper prediction bound L^95 effectively identifies extreme long-runout events, achieving recall values of 0.974 and 0.900 for L > 300 m and L > 500 m, respectively. SHAP analysis reveals that elevation difference H, source volume V, and energy-related derived features dominate both median run-out prediction and upper-tail risk behavior, while slope-related variables mainly influence predictive uncertainty and exceedance-risk levels. These results demonstrate that the proposed CQR-EVT-XAI framework provides a practical workflow for calibrated uncertainty quantification, tail-risk identification, and explainable decision support in rainfall-induced landslide run-out risk assessment. Full article
(This article belongs to the Special Issue Water-Related Landslide Hazard Process and Its Triggering Events—2nd Edition)
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