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Susceptibility Assessment of Landslides in the Loess Plateau Based on Machine Learning Models: A Case Study of Xining City

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  • Li He

    (Key Laboratory of the Northern Qinghai–Tibet Plateau Geological Processes and Mineral Resources, Xining 810000, China
    College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610000, China)

  • Xiantan Wu

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Zhengwei He

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Dongjian Xue

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Fang Luo

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Wenqian Bai

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Guichuan Kang

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Xin Chen

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

  • Yuxiang Zhang

    (College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China)

Abstract

Landslide susceptibility assessment can effectively predict the spatial distribution of potential landslides, which is of great significance in fields such as geological disaster prevention, urban planning, etc. Taking Xining City as an example, based on GF-2 remote sensing image data and combined with field survey data, this study delineated the spatial distribution range of developed landslides. Key factors controlling landslides were then extracted to establish a landslide susceptibility assessment index system. Based on this, the frequency ratio (FR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models were applied to spatially predict landslide susceptibility with slope units as the basis. The main results are as follows: (1) The overall spatial distribution of landslide susceptibility classes in Xining City is consistent, but the differences between different landslide susceptibility classes are significant. (2) The high-susceptibility area predicted by the FR-RF model is the largest, accounting for 15.48% of the total study area. The prediction results of the FR-ANN and FR-SVM models are more similar, with high-susceptibility areas accounting for 13.96% and 12.97%, respectively. (3) The accuracy verification results show that all three coupled models have good spatial prediction capabilities in the study area. The order of landslide susceptibility prediction capabilities from high to low is FR-RF model > FR-ANN model > FR-SVM model. This indicates that in the study area, the FR-RF model is more suitable for carrying out landslide susceptibility assessment.

Suggested Citation

  • Li He & Xiantan Wu & Zhengwei He & Dongjian Xue & Fang Luo & Wenqian Bai & Guichuan Kang & Xin Chen & Yuxiang Zhang, 2023. "Susceptibility Assessment of Landslides in the Loess Plateau Based on Machine Learning Models: A Case Study of Xining City," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14761-:d:1257773
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    References listed on IDEAS

    as
    1. 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.
    2. Siti Norsakinah Selamat & Nuriah Abd Majid & Mohd Raihan Taha & Ashraf Osman, 2022. "Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia," Land, MDPI, vol. 11(6), pages 1-21, June.
    3. 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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