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Comparison of Effects between Different Weight Calculation Methods for Improving Regional Landslide Susceptibility—A Case Study from Xingshan County of China

Author

Listed:
  • Bo Cao

    (College of Mining, Liaoning Technical University, Fuxin 123000, China)

  • Qingyi Li

    (College of Mining, Liaoning Technical University, Fuxin 123000, China)

  • Yuhang Zhu

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

Abstract

The information value (IV) model is a conventional method for landslide susceptibility prediction (LSP). However, it is inconsistent with the actual situation to regard all conditioning factors as equally weighted in the modeling process. In view of this, this paper studied the optimization effect of different weight calculation methods for IV model. Xingshan County, a typical landslide-prone area located in Hubei Province, China, was taken as a case study. The procedure was as follows: First, six conditioning factors, including elevation, slope angle, aspect, curvature, distance to river, and distance to road, were selected to form an evaluation factor library for analyzing the landslide susceptibility. Then, the weight of factors was calculated by fuzzy analytical hierarchy process (FAHP) and principal component analysis (PCA). On this basis, combined with the IV model, two weighted IV models (FAHP-IV model and PCA-IV model) were formed for LSP. The results shows that the optimization effect of PCA was the best. Moreover, compared with the IV-only model (AUC = 0.71), the FAHP-IV model (AUC = 0.76) and PCA-IV model (AUC = 0.79) performed better. The outcome also provided a feasible way for the study of regional LSP.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:11092-:d:907236
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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.
    2. Rui-Xuan Tang & E-Chuan Yan & Tao Wen & Xiao-Meng Yin & Wei Tang, 2021. "Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 13(7), pages 1-25, March.
    3. Christos Polykretis & Manolis G. Grillakis & Athanasios V. Argyriou & Nikos Papadopoulos & Dimitrios D. Alexakis, 2021. "Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece," Land, MDPI, vol. 10(9), pages 1-23, September.
    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.
    5. Thomas Stanley & Dalia B. Kirschbaum, 2017. "A heuristic approach to global 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. 87(1), pages 145-164, May.
    6. Suhua Zhou & Shuaikang Zhou & Xin Tan, 2020. "Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model," Land, MDPI, vol. 9(12), pages 1-22, December.
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