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Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China

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  • Jinming Zhang

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

  • Jianxi Qian

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

  • Yuefeng Lu

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China
    National Center of Technology Innovation for Comprehensive Utilization of Saline-Alkali Land, Dongying 257300, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Xueyuan Li

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

  • Zhenqi Song

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

Abstract

Landslides are among the most prevalent geological hazards and are characterized by their high frequency, significant destructive potential, and considerable incident rate. Annually, these events lead to substantial casualties and property losses. Thus, conducting landslide susceptibility assessments in the regions vulnerable to such hazards has become crucial. In recent years, the coupling of traditional statistical methods with machine learning techniques has shown significant advantages in assessing landslide risk. This study focused on Sichuan Province, China, a region characterized by its vast area and diverse climatic and geological conditions. We selected 13 influencing factors for the analysis: elevation, slope, aspect, plan curve, profile curve, valley depth, precipitation, the stream power index (SPI), the topographic wetness index (TWI), the topographic position index (TPI), surface roughness, fractional vegetation cover (FVC), and slope height. This study incorporated the certainty factor method (CF), the information value method (IV), and their coupling with the decision tree C5.0 model (DT) and a logistic regression model (LR) as follows: IV-LR, IV-DT, CF-LR, and CF-DT. The results, validated by an ROC curve analysis, demonstrate that the evaluation accuracy of all six models exceeded 0.750 (AUC > 0.750). The IV-LR model exhibited the highest accuracy, with an AUC of 0.848. When comparing the accuracy among the models, it is evident that the coupling models outperformed the individual statistical models. Based on the results of the six models, a landslide susceptibility map was generated, categorized into five levels. High and very high landslide risk zones are mainly concentrated in the eastern and southeastern regions, covering nearly half of Sichuan Province. Medium-risk areas form linear distributions from northeast to southwest, occupying a smaller proportion of the area. Extremely low- and low-risk zones are predominantly located in the western and northwestern regions. The density of the landslide points increases with higher risk levels across the regions. This further validates the suitability of this research methodology for landslide susceptibility studies on a large scale. Consequently, this methodology can provide crucial insights for landslide prevention and mitigation efforts in this region.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6803-:d:1452442
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    References listed on IDEAS

    as
    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. 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.
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    4. Weidong Wang & Zhuolei He & Zheng Han & Yange Li & Jie Dou & Jianling Huang, 2020. "Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China," 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. 103(3), pages 3239-3261, September.
    5. Yongwei Li & Xianmin Wang & Hang Mao, 2020. "Influence of human activity on landslide susceptibility development in the Three Gorges area," 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. 104(3), pages 2115-2151, December.
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