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Geosciences
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Landslide Susceptibility Mapping, Hazard Assessment and Risk Evaluation
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Landslide Susceptibility Mapping, Hazard Assessment and Risk Evaluation

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 944

Special Issue Editor

Dr. Prabin Kayastha

E-Mail Website
Guest Editor
Campbelltown City Council, Campbelltown, NSW 2560, Australia
Interests: landslides; floods; hydrology; climate change; hydrogeology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Landslide susceptibility, hazard, and risk are growing research fields, gaining significance due to the widespread effects of landslides globally. These natural disasters have caused considerable distress to communities in affected regions. The Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 has highlighted the shift from a disaster management approach to a risk-focused perspective. In light of this, this Special Issue aims to fill a gap in the knowledge by addressing several critical aspects of landslides:
a) Landslide susceptibility mapping, utilizing various methods such as heuristic, statistical, machine learning, and deterministic approaches.
b) Landslide hazard assessment, which determines the likelihood of landslides impacting a specific area or location within a defined timeframe.
c) Landslide risk evaluation, considering the interplay of landslide susceptibility with different triggering factors, exposure (elements at risk), and vulnerability (of those elements at risk).

These areas are crucial for enhancing our understanding of landslide behavior and improving disaster preparedness and response strategies. 

Dr. Prabin Kayastha
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. Geosciences is an international peer-reviewed open access monthly 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 1800 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 susceptibility mapping
  • landslide hazard assessment
  • landslide risk evaluation
  • landslide vulnerability
  • disaster management

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Published Papers (2 papers)

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Research

24 pages, 7850 KiB  
Article
A Probability-Based Framework for Evaluating Slope Failure Under Rainfall Using Coupled Finite Element Analysis
by Nadarajah Ravichandran and Tharshikka Vickneswaran
Geosciences 2025, 15(4), 118; https://doi.org/10.3390/geosciences15040118 - 26 Mar 2025
Viewed by 283
Abstract
Rainfall is one of the major causes of geological hazards such as landslides and slope failures because it decreases shear strength along the failure surface and increases the driving force of the sliding mass due to the movement of the wetting front in [...] Read more.
Rainfall is one of the major causes of geological hazards such as landslides and slope failures because it decreases shear strength along the failure surface and increases the driving force of the sliding mass due to the movement of the wetting front in the geological media. Deterministic limit equilibrium methods are typically used to evaluate the stability of slopes in terms of Factor of Safety (FoS), considering the worst-case scenario. However, a coupled flow deformation analysis procedure combined with a probabilistic method is required to consider the temporal and spatial variations in the soil properties due to water infiltration and to evaluate the probability of slope failure. The study aims to develop a probabilistic framework for evaluating the probability of failure of an earth slope using the response surface derived from sample data generated from a coupled flow–deformation finite element (FE) program considering uncertain rainfall characteristics. Finite slopes with 1.5H:1V and 2H:1V slope ratios composed of sandy soil were analyzed considering the possible variations in soil and rainfall parameters. Based on the FE results, a response surface was developed for the FoS as a function of soil and rainfall parameters. The response surface was utilized to generate random scenarios and calculate the failure probability using Monte Carlo Simulation (MCS). The results obtained from the MCS were compared using the First-Order Reliability Method (FORM). The results indicated that the total probability of failure predicted by MCS was closer to the probability of failure by FORM. The total probability of failure predicted from MSC and FORM were 0.0633 and 0.0640 for the 1.5:1 slope and 0.0249 and 0.0229 for the 2:1 slope, respectively. This level of probability of failure was deemed unsatisfactory to poor based on the criteria by the US Army Corps of Engineers. Therefore, the proposed framework provides a valuable tool from the probabilistic perspective for assessing the performance level of slopes subjected to uncertain rainfall conditions. Full article
(This article belongs to the Special Issue Landslide Susceptibility Mapping, Hazard Assessment and Risk Evaluation)
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26 pages, 10461 KiB  
Article
Modeling ANN-Based Estimations of Probabilistic-Based Failure Soil Depths for Rainfall-Induced Shallow Landslides Due to Uncertainties in Rainfall Factors
by Shiang-Jen Wu, Syue-Rou Chen and Cheng-Der Wang
Geosciences 2025, 15(3), 88; https://doi.org/10.3390/geosciences15030088 - 1 Mar 2025
Viewed by 378
Abstract
In this study, an ANN-derived innovative model was developed for estimating the failure soil depths of rainfall-induced shallow landslide events, named the SM_EFD_LS model. The proposed SM_EFD_LS model was created using the modified ANN model via the genetic algorithm calibration approach (GA-SA) with [...] Read more.
In this study, an ANN-derived innovative model was developed for estimating the failure soil depths of rainfall-induced shallow landslide events, named the SM_EFD_LS model. The proposed SM_EFD_LS model was created using the modified ANN model via the genetic algorithm calibration approach (GA-SA) with multiple transfer functions (MTFs) (ANN_GA-SA_MTF) with a significant number of failure soil depths and corresponding rainfall factors. Ten shallow landslide-susceptible spots in the Jhuokou watershed in southern Taiwan were selected as the study area. The associated 1000 simulations of rainfall-induced shallow landslide events were used in the model’s development and validation. The model validation results indicate that the validated failure soil depths are mainly located within the resulting 60% confidence intervals from the proposed SM_EFD_LS model. Moreover, the estimated failure depths resemble the validated ones, with acceptable averages of the absolute error (RMSE) and relative error (MRE) (11 cm and 0.06) and a high model reliability index of 1.2. In the future, the resulting probabilistic-based failure soil depths obtained using the proposed SM_EFD_LS model could be introduced with the desired reliability needed for early landslide warning and prevention systems. Full article
(This article belongs to the Special Issue Landslide Susceptibility Mapping, Hazard Assessment and Risk Evaluation)
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