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Improving the Accuracy of Regional Engineering Disturbance Disaster Susceptibility by Optimizing Weight Calculation Methods—A Case Study in the Himalayan Area, China

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  • Yewei Song

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Innovation Academy for Earth Sciences, Chinese Academy of Sciences, Beijing 100029, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jie Guo

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Innovation Academy for Earth Sciences, Chinese Academy of Sciences, Beijing 100029, China)

  • Fengshan Ma

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Innovation Academy for Earth Sciences, Chinese Academy of Sciences, Beijing 100029, China)

  • Jia Liu

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Innovation Academy for Earth Sciences, Chinese Academy of Sciences, Beijing 100029, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Guang Li

    (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Innovation Academy for Earth Sciences, Chinese Academy of Sciences, Beijing 100029, China)

Abstract

The information value method is widely used in predicting the susceptibility of geological disasters. However, most susceptibility evaluation models assume that the weight of each influencing factor is equal, which is inconsistent with the actual situation. Therefore, this paper studies the optimization effect of weight calculation method on the information value model. Engineering disturbance disasters are developing in the Himalayan alpine valley in southeastern Tibet. First of all, this paper takes this as the research object and builds a database of engineering disturbance disasters in southeast Tibet through long-term on-site investigation. Then, the relationship between the influencing factors such as slope, aspect, relief, elevation, engineering geological rock formation, rainfall, temperature, and seismic peak acceleration and the distribution of engineering disturbance disasters is analyzed. Finally, the principal component analysis method and logistic regression method are employed to calculate the weight coefficients. Moreover, the susceptibility of engineering disturbance disasters is predicted using the information value model (IV-Only), as well as two weighted information value models (PCA-IV and LR-IV). In addition, the accuracy of these three susceptibility evaluation models is assessed based on two evaluation indexes. The results show that: compared with the equal weight method and the principal component analysis method, the logistic regression method has the highest accuracy. According to the weight coefficient, the control factors of engineering disturbance disasters in the Himalayan alpine canyon area are determined to be slope, aspect, rainfall, and elevation. The research results provide a reference method for the optimization of susceptibility evaluation model.

Suggested Citation

  • Yewei Song & Jie Guo & Fengshan Ma & Jia Liu & Guang Li, 2023. "Improving the Accuracy of Regional Engineering Disturbance Disaster Susceptibility by Optimizing Weight Calculation Methods—A Case Study in the Himalayan Area, China," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10669-:d:1188192
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    References listed on IDEAS

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    1. Bo Cao & Qingyi Li & Yuhang Zhu, 2022. "Comparison of Effects between Different Weight Calculation Methods for Improving Regional Landslide Susceptibility—A Case Study from Xingshan County of China," Sustainability, MDPI, vol. 14(17), pages 1-15, September.
    2. Sudatta Wadadar & Bhabani Prasad Mukhopadhyay, 2022. "GIS-based landslide susceptibility zonation and comparative analysis using analytical hierarchy process and conventional weighting-based multivariate statistical methods in the Lachung River Basin, No," 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. 113(2), pages 1199-1236, September.
    3. Krishna Devkota & Amar Regmi & Hamid Pourghasemi & Kohki Yoshida & Biswajeet Pradhan & In Ryu & Megh Dhital & Omar Althuwaynee, 2013. "Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya," 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. 65(1), pages 135-165, January.
    4. Sina Paryani & Aminreza Neshat & Saman Javadi & Biswajeet Pradhan, 2020. "Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping," 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(2), pages 1961-1988, September.
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