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Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping

Author

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  • Rui-Xuan Tang

    (School of Geosciences, Yangtze University, Wuhan 430100, China)

  • E-Chuan Yan

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

  • Tao Wen

    (School of Geosciences, Yangtze University, Wuhan 430100, China)

  • Xiao-Meng Yin

    (College of Architecture and Civil Engineering, Xinyang Normal University, Xinyang 464000, China)

  • Wei Tang

    (School of Urban and Rural Planning and Architectural Engineering, Guiyang University, Guiyang 550005, China)

Abstract

This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:3803-:d:526681
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    References listed on IDEAS

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    2. Yigen Qin & Genlan Yang & Kunpeng Lu & Qianzheng Sun & Jin Xie & Yunwu Wu, 2021. "Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China," Sustainability, MDPI, vol. 13(11), pages 1-20, June.
    3. 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.
    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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