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Geological Disaster Susceptibility Evaluation of a Random-Forest-Weighted Deterministic Coefficient Model

Author

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  • Shaohan Zhang

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
    Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China)

  • Shucheng Tan

    (Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China
    School of Earth Science, Yunnan University, Kunming 650500, China)

  • Jinxuan Zhou

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
    Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China)

  • Yongqi Sun

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China)

  • Duanyu Ding

    (Faculty of Architecture and City Planning, Kunming University of Science and Technology, Kunming 650500, China)

  • Jun Li

    (Yunnan Architectural Engineering Design Company Limited, Kunming 650501, China)

Abstract

An assessment of regional vulnerability to geological disasters can directly indicate the extent and intensity of risks within the study area; thus, providing precise guidance for disaster management efforts. However, in the evaluation of geological disaster susceptibility using a single deterministic coefficient model, the direct superimposition of deterministic coefficient values for each evaluation factor, without considering their objective weights, can impact the accuracy of susceptibility zoning outcomes. To address this limitation, this research proposes a novel approach: geological disaster susceptibility evaluation using a random-forest-weighted deterministic coefficient model. In this method, the objective weight of each evaluation factor is calculated based on a deterministic coefficient model and a parameter-optimized random forest model. By weighting and superimposing the deterministic coefficient values of each evaluation factor, a comprehensive deterministic coefficient map is generated. This map is further divided using the natural breakpoint method to obtain a geological disaster susceptibility zoning map. To validate the accuracy of the evaluation results, partition statistics and the ROC (Receiver Operating Characteristic) curve of the test sample points are utilized. The findings demonstrate that the model performs well in evaluating geological disaster susceptibility in Huize County. The evaluation results are considered reliable and accurate, highlighting the effectiveness of the proposed approach for assessing and zoning geological disaster susceptibility in the region.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12691-:d:1222454
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    References listed on IDEAS

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    1. Jinxuan Zhou & Shucheng Tan & Jun Li & Jian Xu & Chao Wang & Hui Ye, 2023. "Landslide Susceptibility Assessment Using the Analytic Hierarchy Process (AHP): A Case Study of a Construction Site for Photovoltaic Power Generation in Yunxian County, Southwest China," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    2. 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.
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    Cited by:

    1. Xin Zhang & Lijun Jiang & Wei Deng & Zhile Shu & Meiben Gao & Guichuan Liu, 2024. "Risk Assessment of Geological Hazards in the Alpine Gorge Region and Its Influencing Factors: A Case Study of Jiulong County, China," Sustainability, MDPI, vol. 16(5), pages 1-16, February.
    2. Xiang Zhang & Minghui Zhang & Xin Liu & Berhanu Keno Terfa & Won-Ho Nam & Xihui Gu & Xu Zhang & Chao Wang & Jian Yang & Peng Wang & Chenghong Hu & Wenkui Wu & Nengcheng Chen, 2024. "Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11485-11525, October.
    3. Ruixia Ma & Yan Lyu & Tianbao Chen & Qian Zhang, 2023. "Preliminary Risk Assessment of Geological Disasters in Qinglong Gorge Scenic Area of Taihang Mountain with GIS Based on Analytic Hierarchy Process and Logistic Regression Model," Sustainability, MDPI, vol. 15(22), pages 1-19, November.
    4. Jinming Zhang & Jianxi Qian & Yuefeng Lu & Xueyuan Li & Zhenqi Song, 2024. "Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China," Sustainability, MDPI, vol. 16(16), pages 1-22, August.

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