Advances in Modelling of Landslide Hazards

A special issue of Modelling (ISSN 2673-3951).

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2688

Special Issue Editors


E-Mail Website1 Website2
Guest Editor
1. CEREMA (Centre d'études et d'expertise sur les risques, l'environnement, la mobilité et l'aménagement, Agence de Sophia Antipolis), 500 route des Lucioles - CS 80125 Valbonne - Sophia Antipolis CEDEX, France
2. IFSTTAR, Dpt Geotechnical Engineering, Environment, Natural hazards and Earth sciences Department Dpt GERS, Gustave Eiffel University, Marne-la-Vallée, France
Interests: wave propagation; soils mechanics; landslides; vibrations

E-Mail Website
Guest Editor
Department of Earth Sciences, Sapienza University of Rome and CERI—Research Centre for Geological Risks, Piazzale Aldo Moro 5, 00185 Rome, Italy
Interests: engineering geology; natural hazards; landslide; local seismic response; numerical modelling
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Special Issue Information

Dear Colleagues,

During the last few decades, the rapid improvement in digital techniques such as GIS and photogrammetry, as well as of remote surveying techniques, lends support to landslide inventorying and geodatabase construction as suitable tools for deriving and validating landslide scenarios from a multihazard perspective. Advanced numerical methods are contributing to quantifying slope stability as well as landslide mass interaction with different triggering actions while analytical solutions and rheological equations were significantly improved for stress–strain numerical modeling in 2D or 3D domains by continuum and discontinuum approaches. Numerical models, accounting for geometrical, mechanical, and hydrogeological features, are suitable for assessing the susceptibility to first-time slope failures or landslide reactivations as well as for providing landslide evolution over time from back- to forward-analysis, depending on possible natural or anthropogenic factors, such as meteoclimatic conditions and environmental exploitation due to human activities. These analyses provide a quantitative contribution for mapping landslide susceptibility, also at regional scale, in addition landslide multihazard scenarios and represent useful tools for risk management.

This Special Issue focuses on 1) illustrating the available methodological approaches for empirical, analytical, and numerical models to derive landslide scenarios in a multihazard perspective and 2) highlighting the limits and potential of numerical modeling in quantifying slope stability conditions or landslide evolution over time through case studies referred to local or regional contexts.

Dr. Luca Lenti
Dr. Salvatore Martino
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. Modelling is an international peer-reviewed open access quarterly 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 1000 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 hazard
  • slope stability
  • empirical, analytical, and numerical modeling
  • multihazard scenarios

Published Papers (1 paper)

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Review

28 pages, 2308 KiB  
Review
Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review
by Haoding Xu, Xuzhen He, Feng Shan, Gang Niu and Daichao Sheng
Modelling 2023, 4(4), 426-453; https://0-doi-org.brum.beds.ac.uk/10.3390/modelling4040025 - 01 Oct 2023
Cited by 1 | Viewed by 1396
Abstract
In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results in higher costs, and thus, a stochastic analysis considering uncertainty and spatial variability [...] Read more.
In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results in higher costs, and thus, a stochastic analysis considering uncertainty and spatial variability was developed to reduce costs. In the past few decades, machine learning has been greatly developed and extensively used in stochastic slope stability analysis, particularly used as surrogate models to improve computational efficiency. To better summarize the current application of machine learning and future research, this paper reviews 159 studies of supervised learning published in the past 20 years. The achievements of machine learning methods are summarized from two aspects—safety factor prediction and slope stability classification. Four potential research challenges and suggestions are also given. Full article
(This article belongs to the Special Issue Advances in Modelling of Landslide Hazards)
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