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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 12 - 2024 | doi: 10.3389/feart.2024.1519771
This article is part of the Research Topic Failure Analysis and Risk Assessment of Natural Disasters Through Machine Learning and Numerical Simulation: Volume IV View all 16 articles

A comparative study of intelligent prediction models for landslide susceptibility: Random Forest and Support Vector Machine

Provisionally accepted
yuwei liu yuwei liu Yuling Xu Yuling Xu *jun huang jun huang haiting liu haiting liu yu fang yu fang yu yu yu yu
  • Natural Resources Policy Investigation and Evaluation Center of Jiangxi Province, Nanchang, Jiangxi, China

The final, formatted version of the article will be published soon.

    Colluvial landslides widely developed in mountainous and hilly areas have the characteristics of mass occurrence and sudden occurrence. How to reveal the spatial distribution rules of potential landslides quickly and accurately is of great significance for landslide warning and prevention in the study area. Landslide susceptibility prediction (LSP) modeling provides an effective way to reveal the spatial distribution of regional landslides, however, it is difficult to accurately divide slope units and select prediction models in the processes of LSP modeling. To solve these problems, this paper takes the widely developed colluvial landslides in Dingnan County, Jiangxi Province, China as the research object. Firstly, the multi-scale segmentation(MSS) algorithm is used to divide Dingnan County into 100,000 slope units, to improve the efficiency and accuracy of slope unit division. Secondly, 18 environmental factors with abundant types and clear meanings, including topography, lithology and hydrological environment factors, were selected as input variables of LSP models. Then, a widely representative Support Vector Machine (SVM) and Random Forest (RF) models were selected to explore the difference characteristics of various machine learning models in predicting landslide susceptibility. Finally, the comprehensive evaluation method is proposed to compare the accuracy of various slope unit-based machine learning methods for LSP. The results show that the MSS algorithm can divide slope units in Dingnan County efficiently and accurately. The RF model (AUC=0.896) has a higher LSP accuracy than that of the SVM model (AUC=0.871), and the landslide susceptibility indexes (LSI) predicted by the RF model have a smaller mean value and a larger standard deviation than those of the SVM model. Conclusively, the overall performance of RF model in predicting landslide susceptibility is higher than that of SVM model.

    Keywords: Landslide susceptibility prediction, machine learning, multi-scale segmentation method, random forest, Support vector machine

    Received: 30 Oct 2024; Accepted: 28 Nov 2024.

    Copyright: © 2024 liu, Xu, huang, liu, fang and yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yuling Xu, Natural Resources Policy Investigation and Evaluation Center of Jiangxi Province, Nanchang, Jiangxi, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.