Rainfall-Induced Landslides and Natural Geohazards

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 2106

Special Issue Editors


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Guest Editor
College of Resources and Environmental Engineering, Guizhou University, Guiyang, China
Interests: hydrodynamic landslide; landslide prediction; slope stability; machine learning; multi-source data mining in geosciences

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Guest Editor
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Interests: geohazards; prediction; risk assessment; remote sensing; landslides
Special Issues, Collections and Topics in MDPI journals
School of Civil Engineering, Zhengzhou University, Zhengzhou, China
Interests: landslide; stabilizing pile; water-rock interaction; geotechnical uncertainty; cyclic wetting-drying action; rock mechanics; multi-scale structures of geomaterials

Special Issue Information

Dear Colleagues,

Rainfall is the main trigger factor for various natural geohazards, such as landslides, rock avalanches, debris flows, and ground collapses. In recent years, with the frequent occurrence of extreme rainfall events, the natural geohazards have correspondingly increased around the world. This not only constantly poses a huge threat to human life and property, but also seriously damages the balance of natural ecosystems. Thus, it is of great significance to explore the mechanisms, evaluation methods, and prediction models of rainfall-induced landslides and natural geohazards for disaster risk management and ecological environment protection.

This Special Issue invites the submission of original research papers covering the latest findings and progress in the field of rainfall-induced landslides and natural geohazards. The topics of interest include but are not limited to:

  • Mechanism analysis of rainfall-induced natural geohazards using physical or data-driven methods.
  • Numerical modeling and stability analysis of natural geohazards under extreme rainfall conditions.
  • Spatial/temporal prediction models for natural geohazards considering extreme rainfall events.

Dr. Linwei Li
Dr. Fasheng Miao
Dr. Wenmin Yao
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

  • rainfall-induced landslide
  • rainfall-induced natural geohazards
  • numerical modeling
  • mechanism analysis
  • stability analysis
  • spatial prediction
  • temporal prediction
  • data-driven method

Published Papers (2 papers)

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Research

13 pages, 6586 KiB  
Article
An Inversion Study of Reservoir Colluvial Landslide Permeability Coefficient by Combining Physical Model and Data-Driven Models
by Xiaopeng Yue, Yankun Wang and Tao Wen
Water 2024, 16(5), 686; https://doi.org/10.3390/w16050686 - 26 Feb 2024
Viewed by 668
Abstract
The saturated permeability coefficient (ks) is a key parameter for evaluating the seepage and stability of reservoir colluvial landslides. However, ks values obtained from traditional experimental methods are often characterized by large variations and low representativeness. As a result, there are [...] Read more.
The saturated permeability coefficient (ks) is a key parameter for evaluating the seepage and stability of reservoir colluvial landslides. However, ks values obtained from traditional experimental methods are often characterized by large variations and low representativeness. As a result, there are significant deviations from actual observations when used in seepage field calculations for reservoir landslide analysis. This study proposes an intelligent inversion method that combines a physical model and a data-driven model for reservoir landslide ks based on actual groundwater level (GWL) monitoring data. This method combines Latin Hypercube Sampling (LHS), unsaturated flow finite element (FE) analysis, particle swarm optimization algorithm (PSO), and kernel extreme learning machine model (KELM). Taking the Hongyanzi landslide in Sichuan Province, China, as the research object, the GWL of the landslide under different ks was first obtained by LHS and transient seepage FE analysis. Then, a nonlinear functional relationship between ks and the landslide GWL was fitted based on the PSO-KELM model. Finally, the optimal landslide ks was obtained by minimizing the root-mean-squared error between the predicted and actual GWL using the PSO. A global sensitivity analysis was also conducted on the ks of different rock and soil layers to reveal their control rules on the calculation of landslide GWL. The research results demonstrate the feasibility of the proposed method and provide valuable information for similar landslides in practice. Full article
(This article belongs to the Special Issue Rainfall-Induced Landslides and Natural Geohazards)
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14 pages, 3222 KiB  
Article
Modeling Rainfall Impact on Slope Stability: Computational Insights into Displacement and Stress Dynamics
by Jingmei Zong, Changjun Zhang, Leifei Liu and Lulu Liu
Water 2024, 16(4), 554; https://doi.org/10.3390/w16040554 - 11 Feb 2024
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Abstract
The susceptibility of loess slopes to collapses, landslides, and sinkholes is a global concern. Rainfall is a key factor exacerbating these issues and affecting slope stability. In regions experiencing significant infrastructure and urban growth, understanding and mitigating rainfall effects on loess landslides is [...] Read more.
The susceptibility of loess slopes to collapses, landslides, and sinkholes is a global concern. Rainfall is a key factor exacerbating these issues and affecting slope stability. In regions experiencing significant infrastructure and urban growth, understanding and mitigating rainfall effects on loess landslides is crucial. ADINA numerical software 9 was utilized to explore rain-induced erosion’s influence on landslide dynamics. The simulations were based on local rainfall trends. The rainfall intensities examined were as follows: 200 mm/day, 300 mm/day, and 400 mm/day. The results indicate a pronounced impact of rainfall intensity on both the movement and stress levels within the slope. Higher rainfall intensities lead to increased movement and a wider stress impact area at the base of the slope. It was observed that surface movement is minimal at the slope crest but increases towards the bottom, with the greatest movement seen at the slope’s base. Full article
(This article belongs to the Special Issue Rainfall-Induced Landslides and Natural Geohazards)
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