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Landslide Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example

Author

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  • Haishan Wang

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

  • Jian Xu

    (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)

Abstract

Shuangbai County, located in Yunnan Province, Southwest China, possesses a complex and diverse geological environment and experiences frequent landslide disasters. As a significant area for disaster prevention and control, it is crucial to assess the susceptibility of landslides for effective geological disaster prevention, urban planning, and development. This research focuses on eleven influencing factors, including elevation, slope, slope direction, rainfall, NDVI, and distance from faults, selected as evaluation indexes. The assessment model is constructed using the information quantity method and the information quantity logistic regression coupling method to analyze the landslide susceptibility in Shuangbai County. The entire region’s landslide susceptibility is classified into four categories: not likely to occur, low susceptibility, medium susceptibility, and high susceptibility. The accuracy and reasonableness of the models are tested and compared. The results indicate that the coupled information–logistic regression model (80.0% accuracy) outperforms the single information model (74.2% accuracy). Moreover, the density of disaster points in the high-susceptibility area of the coupled model is higher, making it more reasonable. Thus, this model can serve as a valuable tool for evaluating regional landslide susceptibility in Shuangbai County and as a basis for disaster mitigation planning by relevant authorities.

Suggested Citation

  • Haishan Wang & Jian Xu & Shucheng Tan & Jinxuan Zhou, 2023. "Landslide Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example," Sustainability, MDPI, vol. 15(16), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12449-:d:1218389
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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. Hariklia D. Skilodimou & George D. Bathrellos & Efterpi Koskeridou & Konstantinos Soukis & Dimitrios Rozos, 2018. "Physical and Anthropogenic Factors Related to Landslide Activity in the Northern Peloponnese, Greece," Land, MDPI, vol. 7(3), pages 1-18, July.
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    4. Yanrong Liu & Zhongqiu Meng & Lei Zhu & Di Hu & Handong He, 2023. "Optimizing the Sample Selection of Machine Learning Models for Landslide Susceptibility Prediction Using Information Value Models in the Dabie Mountain Area of Anhui, China," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
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