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Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture

Author

Listed:
  • Yimin Li

    (College of Earth Sciences, Yunnan University, Kunming 650500, China
    Engineering Research Center of Domestic High-Resolution Satellite Remote Sensing Geology for Universities of Yunnan Province, Kunming 650500, China)

  • Xuanlun Deng

    (College of Earth Sciences, Yunnan University, Kunming 650500, China)

  • Peikun Ji

    (College of Earth Sciences, Yunnan University, Kunming 650500, China)

  • Yiming Yang

    (College of Earth Sciences, Yunnan University, Kunming 650500, China)

  • Wenxue Jiang

    (College of Earth Sciences, Yunnan University, Kunming 650500, China)

  • Zhifang Zhao

    (College of Earth Sciences, Yunnan University, Kunming 650500, China
    Engineering Research Center of Domestic High-Resolution Satellite Remote Sensing Geology for Universities of Yunnan Province, Kunming 650500, China)

Abstract

At present, landslide susceptibility assessment (LSA) based on landslide characteristics in different areas is an effective measure for landslide management. Nujiang Prefecture in China has steep mountain slopes, a large amount of water and loose soil, and frequent landslide disasters, which have caused a large number of casualties and economic losses. This paper aims to understand the characteristics and formation mechanism of regional landslides through the evaluation of landslide susceptibility so as to provide relevant references and suggestions for spatial planning and disaster prevention and mitigation in Nujiang Prefecture. Based on the grid cell, this study selected 10 parameters, namely elevation, slope, aspect, lithology, proximity to faults, proximity to road, proximity to rivers, normalized difference vegetation index (NDVI), land-use type, and precipitation. Support vector machine (SVM), certainty factor method (CF), and deterministic coefficient method–support vector machine (CF-SVM) were used to evaluate the landslide susceptibility in Nujiang Prefecture. According to these three models, the study area was divided into five landslide susceptibility grades, including extremely high susceptibility, high susceptibility, moderate susceptibility, low susceptibility, and very low susceptibility. Receiver operating characteristic curve (ROC) was applied to verify the accuracy of the model. The results showed that CF model (ROC = 0.865), SVM model (ROC = 0.892), CF-SVM model (ROC = 0.925), and CF-SVM model showed better performance. Therefore, CF-SVM model results were selected for analysis. The study found that the characteristics of high and extremely high landslide-prone areas in Nujiang Prefecture have the following characteristics: intense human activities, large density of buildings and arable land, rich water resources, good economic development, perfect transportation facilities, and complex topography and landform. In addition, there is a finding inconsistent with our common sense that the distribution of landslide disasters in the study area does not decrease with the increase of NDVI value. This is because the Nujiang River basin is a high mountain canyon area with low rock strength, barren soil, and underdeveloped vegetation and root system. In an area with large slope, the probability of landslide disaster will increase with the increase of NDVI. The CF-SVM coupling model adopted in this study is a good first attempt in the study of landslide hazard susceptibility in Nujiang Prefecture.

Suggested Citation

  • Yimin Li & Xuanlun Deng & Peikun Ji & Yiming Yang & Wenxue Jiang & Zhifang Zhao, 2022. "Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture," IJERPH, MDPI, vol. 19(21), pages 1-24, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14248-:d:959133
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    References listed on IDEAS

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