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Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China

Author

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  • Yigen Qin

    (Guangdong Provincial Key Laboratory of Geodynamics and Geohazards, School of Earth Sciences and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
    Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China
    State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China)

  • Genlan Yang

    (College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China)

  • Kunpeng Lu

    (College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China)

  • Qianzheng Sun

    (104 Geological Brigade, Bureau of Geology and Mineral Exploration and Development of Guizhou Province, Duyun 558000, China)

  • Jin Xie

    (College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China)

  • Yunwu Wu

    (Guizhou Kaiyang Economic Development Industry Investment and Development Co. Ltd., Guiyang 550300, China)

Abstract

This study evaluated causative factors in landslide susceptibility assessments and compared the performance of five landslide susceptibility models based on the certainty factor (CF), logistic regression (LR), analytic hierarchy process (AHP), coupled CF–analytic hierarchy process (CF-AHP), and CF–logistic regression (CF-LR). Kaiyang County, China, has complex geological conditions and frequent landslide disasters. Based on field observations, nine influencing factors, namely, altitude, slope, topographic relief, aspect, engineering geological rock group, slope structure, distance to faults, distance to rivers, and normalized difference vegetation index, were extracted using the raster data model. The precision of the five models was tested using the distribution of disaster points for each grade and receiver operating characteristic curve. The results showed that the landslide frequency ratios accounted for more than 75% within the high and very high susceptibility zones according to the model prediction, and the AUC evaluating precision was 0.853, 0.712, 0.871, 0.873, and 0.895, respectively. The accuracy sequencing of the five models was CF-LR > CF-AHP > LR > CF > AHP, indicating that the CF-AHP and CF-LR models are better than the others. This study provides a reliable method for landslide susceptibility mapping at the county-level resolution.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6441-:d:569604
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    4. Siti Norsakinah Selamat & Nuriah Abd Majid & Aizat Mohd Taib, 2023. "A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia," Sustainability, MDPI, vol. 15(1), pages 1-21, January.

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