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Geosciences
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Advances in Landslide Monitoring, Inventory and Susceptibility Mapping
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Advances in Landslide Monitoring, Inventory and Susceptibility Mapping

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2282

Special Issue Editor

Dr. Leonhard Blesius

E-Mail Website
Guest Editor
Department of Geography & Environment, San Francisco State University, 1600 Holloway Avenue, HSS Bldg, Room 283, San Francisco, CA 94132, USA
Interests: environmental systems analysis; fluvial geomorphology; natural hazard; landslides

Special Issue Information

Dear Colleagues,

Mass wasting events are a particularly frequent geomorphological hazard. Landslides is the generic term for these occurrences. While landslides may not be as devastating as some other natural disasters are, collectively, they are responsible for the loss of many lives and economic hardship. It is, therefore, of great importance to monitor individual landslides threatening human habitats, to study and understand the distribution of them across the landscape, and to be able to predict their spatial and temporal occurrences.

New monitoring and mapping technologies and methods to predict mass wasting events are being rapidly developed. These new technologies and methodological advances often involve remote sensing and may include, but are not limited to, UAS-based high resolution imagery, lidar acquisition, and radar systems, novel techniques in image processing, as well as artificial intelligence and machine learning algorithms.

We welcome any contributions to this Special Issue that advance our knowledge about monitoring individual landslides, aid landslide inventories, and improve the mapping of landslide susceptibility using heuristic, statistical, machine learning, or physical methods.

Dr. Leonhard Blesius
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 monitoring
  • landslide inventory
  • landslide susceptibility mapping
  • remote sensing
  • image processing
  • heuristic
  • machine learning
  • physically based methods

Published Papers (2 papers)

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Research

23 pages, 16272 KiB  
Article
A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques—Case Study: Northern Lima Commonwealth, Peru
by Edwin Badillo-Rivera, Manuel Olcese, Ramiro Santiago, Teófilo Poma, Neftalí Muñoz, Carlos Rojas-León, Teodosio Chávez, Luz Eyzaguirre, César Rodríguez and Fernando Oyanguren
Geosciences 2024, 14(6), 168; https://doi.org/10.3390/geosciences14060168 - 14 Jun 2024
Viewed by 510
Abstract
This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). A previous exploration of the topographic variables revealed a high correlation and multicollinearity among some of them, [...] Read more.
This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). A previous exploration of the topographic variables revealed a high correlation and multicollinearity among some of them, which led to dimensionality reduction through a principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being El Niño phenomenon and the second being an earthquake exceeding 8.8 Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of >0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8 Mw, approximately 40% and 35% respectively, of the NLC area would be exposed to the highest hazard levels. The importance of integrating methodologies in mass movement susceptibility models is also emphasized; these methodologies include the correlation analysis, multicollinearity assessment, dimensionality reduction of variables, and coupling statistical models with machine learning models to improve the predictive accuracy of machine learning models. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies. Full article
(This article belongs to the Special Issue Advances in Landslide Monitoring, Inventory and Susceptibility Mapping)
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31 pages, 11012 KiB  
Article
The Open Landslide Project (OLP), a New Inventory of Shallow Landslides for Susceptibility Models: The Autumn 2019 Extreme Rainfall Event in the Langhe-Monferrato Region (Northwestern Italy)
by Michele Licata, Victor Buleo Tebar, Francesco Seitone and Giandomenico Fubelli
Geosciences 2023, 13(10), 289; https://doi.org/10.3390/geosciences13100289 - 23 Sep 2023
Viewed by 1362
Abstract
Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide [...] Read more.
Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide risk management. Using a multidisciplinary approach and open source, multisatellite imagery data, more than 3000 landslides triggered by the extreme rainfall of autumn 2019 in northwestern Italy were systematically mapped. The inventory creation process followed well-defined criteria and underwent rigorous validation to ensure accuracy and reliability. The dataset’s suitability was confirmed through multivariate correlation and Double Pareto probably density function. The OLP inventory effectiveness in assessing landslide risks was proved by the development of a landslide susceptibility model using binary logistic regression. The analysis of rainfall and lithology revealed that regions with lower rainfall levels experienced a higher occurrence of landslides compared to areas with higher peak rainfall. This was attributed to the response of the lithological composition to rainfalls. The findings of this research contribute to the understanding and management of landslide risks in anthropized climate regions. The OLP has proven to be a valuable resource for future geostatistical analysis. Full article
(This article belongs to the Special Issue Advances in Landslide Monitoring, Inventory and Susceptibility Mapping)
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