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Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

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  • Xiaoyi Wu

    (Evaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province & Sichuan Geological Survey, Chengdu 610081, China
    College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China)

  • Yuanbao Song

    (Evaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province & Sichuan Geological Survey, Chengdu 610081, China)

  • Wei Chen

    (Liangshan Prefecture Urban and Rural Land Comprehensive Consolidation and Reserve Center, Liangshan 615050, China)

  • Guichuan Kang

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Rui Qu

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Zhifei Wang

    (College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China)

  • Jiaxian Wang

    (Research Institute of Exploration and Development, PetroChina Southwest Oil & Gas Field Company, Chengdu 610051, China)

  • Pengyi Lv

    (Research Institute of Exploration and Development, PetroChina Southwest Oil & Gas Field Company, Chengdu 610051, China)

  • Han Chen

    (Sichuan Earthquake Agency, Chengdu 610041, China
    Chengdu Institute of Tibetan Plateau Earthquake Research, China Earthquake Administration, Chengdu 610041, China)

Abstract

Landslides seriously threaten human life and property. The rapid and accurate prediction of landslide geological hazard susceptibility is the key to disaster prevention and mitigation. Traditional landslide susceptibility evaluation methods have disadvantages in terms of factor classification and subjective weight determination. Based on this, this paper uses a random forest model built using Python language to predict the landslide susceptibility of Muli County in western Sichuan and outputs the factor weight and model accuracy. The results show that (1) the three most important factors are elevation, distance from the road, and average annual rainfall, and the sum of their weights is 67.54%; (2) the model’s performance is good, with ACC = 99.43%, precision = 99.3%, recall = 99.48%, and F1 = 99.39%; (3) the landslide development and susceptibility zoning factors are basically the same. Therefore, this model can effectively and accurately evaluate regional landslide susceptibility. However, there are some limitations: (1) the landslide information statistical table is incomplete; (2) there are demanding requirements in terms of training concentration relating to the definition of landslide and non-landslide point sets, and the landslide range should be accurately delineated according to field surveys.

Suggested Citation

  • Xiaoyi Wu & Yuanbao Song & Wei Chen & Guichuan Kang & Rui Qu & Zhifei Wang & Jiaxian Wang & Pengyi Lv & Han Chen, 2023. "Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm," Sustainability, MDPI, vol. 15(5), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4328-:d:1083558
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    References listed on IDEAS

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    1. Yue Wang & Deliang Sun & Haijia Wen & Hong Zhang & Fengtai Zhang, 2020. "Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)," IJERPH, MDPI, vol. 17(12), pages 1-39, June.
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    Cited by:

    1. Shaohan Zhang & Shucheng Tan & Jinxuan Zhou & Yongqi Sun & Duanyu Ding & Jun Li, 2023. "Geological Disaster Susceptibility Evaluation of a Random-Forest-Weighted Deterministic Coefficient Model," Sustainability, MDPI, vol. 15(17), pages 1-21, August.
    2. Fucheng Xing & Ning Li & Boju Zhao & Han Xiang & Yutao Chen, 2024. "An Investigation into the Susceptibility to Landslides Using Integrated Learning and Bayesian Optimization: A Case Study of Xichang City," Sustainability, MDPI, vol. 16(20), pages 1-20, October.
    3. Ming Li & Linlong Li & Yangqi Lai & Li He & Zhengwei He & Zhifei Wang, 2023. "Geological Hazard Susceptibility Analysis Based on RF, SVM, and NB Models, Using the Puge Section of the Zemu River Valley as an Example," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    4. Li Zhuo & Yupu Huang & Jing Zheng & Jingjing Cao & Donghu Guo, 2023. "Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance," Sustainability, MDPI, vol. 15(11), pages 1-23, June.

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