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Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China

Author

Listed:
  • Jiangping Gao

    (Geological Hazards Prevention Institute, Gansu Academy of Sciences, Lanzhou 730099, China)

  • Xiangyang Shi

    (Geological Hazards Prevention Institute, Gansu Academy of Sciences, Lanzhou 730099, China)

  • Linghui Li

    (Geological Hazards Prevention Institute, Gansu Academy of Sciences, Lanzhou 730099, China)

  • Ziqiang Zhou

    (Geological Hazards Prevention Institute, Gansu Academy of Sciences, Lanzhou 730099, China)

  • Junfeng Wang

    (College of Grassland Agriculture, Northwest Agriculture and Forestry University, Yangling 712100, China)

Abstract

In recent decades, with the increase in extreme climate duration and the continuous development of urbanization in China, the threat of landslide disasters has become increasingly serious. More and more scholars pay attention to the problem of the prevention of landslide disasters. Therefore, the landslide susceptibility prediction is generated, which can play an important role in the design of land development and urban development schemes in mountainous areas. In this paper, the frequency ratio (FR) model is used to quantitatively analyze the relationship between each factor and the occurrence of landslide (elevation, slope, aspect, plan curvature, profile curvature, distance to faults, rainfall, distance to rivers, soil types, land cover, Normalized Difference Vegetation Index (NDVI) and distance to roads). Based on the analysis of landslide distribution, 12 influencing factors were selected to establish the landslide susceptibility evaluation index system. Historical landslide points were randomly divided into training (70% of the total) and validation (30%) sets. Thereafter, decision tree (DT), logistic regression (LR), and random forest (RF) models were used to generate the landslide susceptibility mapping (LSM), and the predictive performance of the three models was evaluated using receiver operating characteristic (ROC) curves. The FR model results showed that landslides mostly occurred at slopes of 0–15°, elevations of <1000 m, distance to rivers of 0–500 m, rainfall of 750–840 mm, NDVI of 0.8–0.9, distance to roads of 0–500 m, distance to faults of 1500–2000 m and transportation land. Our results also showed that the RF model showed a great capability of identifying areas highly susceptible to landslide, and this model had the greatest reliability. High and very high landslide susceptibility was detected for 29.73% of the land area of Longnan City, Gansu Province, mainly in the eastern, northeastern, and southern regions. The importance ranking of the RF model also revealed that elevation, NDVI, distance to roads, and rainfall dominated the spatial distribution of landslides. Our results could help government agencies and decision-makers make wise decisions for future natural hazard prevention in Longnan City.

Suggested Citation

  • Jiangping Gao & Xiangyang Shi & Linghui Li & Ziqiang Zhou & Junfeng Wang, 2022. "Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16716-:d:1002346
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    References listed on IDEAS

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    1. Binh Thai Pham & Ataollah Shirzadi & Himan Shahabi & Ebrahim Omidvar & Sushant K. Singh & Mehebub Sahana & Dawood Talebpour Asl & Baharin Bin Ahmad & Nguyen Kim Quoc & Saro Lee, 2019. "Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
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