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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 12 - 2024 | doi: 10.3389/feart.2024.1516615
This article is part of the Research Topic Monitoring, Early Warning and Mitigation of Natural and Engineered Slopes – Volume IV View all 24 articles

Spatial distribution prediction of landslide susceptibility based on integrated particle swarm optimization

Provisionally accepted
Qing Zhang Qing Zhang *
  • Xi'an University of Science and Technology, Xi'an, China

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

    Landslide sensitivity prediction relies on multiple environmental factors, making it difficult to obtain accurate prediction results. In order to improve the prediction accuracy of regional landslide sensitivity, a landslide sensitivity spatial distribution prediction method based on integrated particle swarm optimization was studied in Lianhe Village, Jianfeng Town, Shizhong District, Leshan City, Sichuan Province. Based on the determination coefficient, the sensitivity of landslide influencing factors was analyzed, and the weights of the influencing factors were determined. A landslide sensitivity spatial distribution prediction model was established based on support vector machine. By introducing simulated annealing and mutation operations into the particle swarm algorithm, an integrated particle swarm algorithm was obtained to extract high weight features of landslide sensitivity space and generate landslide sensitivity prediction results.The experimental results show that the cumulative value (ACU) of this method for predicting landslide sensitivity is 0.91, which can accurately predict the spatial distribution of landslide sensitivity in the study area and has practical value.

    Keywords: integrated particle swarm optimization1, support vector machine2, landslide Susceptibility3, space distribution4, application value5

    Received: 24 Oct 2024; Accepted: 21 Nov 2024.

    Copyright: © 2024 Zhang. 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: Qing Zhang, Xi'an University of Science and Technology, Xi'an, China

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