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Volume 34 Issue 5
Oct.  2023
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Abstract
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WU Hongyang，ZHOU Chao，LIANG Xin，et al. Assessment of landslide susceptibility mapping based on XGBoost model: A case study of Yanshan Township[J]. The Chinese Journal of Geological Hazard and Control，2023，34(5): 141-152. DOI: 10.16031/j.cnki.issn.1003-8035.202206020
Citation: WU Hongyang，ZHOU Chao，LIANG Xin，et al. Assessment of landslide susceptibility mapping based on XGBoost model: A case study of Yanshan Township[J]. The Chinese Journal of Geological Hazard and Control，2023，34(5): 141-152. DOI: 10.16031/j.cnki.issn.1003-8035.202206020
shu

Assessment of landslide susceptibility mapping based on XGBoost model: A case study of Yanshan Township

  • 1.

    School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei　430078, China

  • 2.

    Research Center of Geohazard Monitoring and Warning in the Three Gorges Reservoir, Chongqing　404199, China

  • 3.

    Faculty of Engineering, China University of Geosciences, Wuhan, Hubei　430074, China

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  • Received Date: June 16, 2022
  • Revised Date: August 25, 2022
  • Available Online: July 12, 2023
    Graphical Abstract
    • Abstract
  • Landslide susceptibility assessment forms the foundation for precise evaluation of landslide risk. To enhance the accuracy and robustness of landslide susceptibility mapping, a state-of-art machine learning algorithm named the extreme gradient boosting model (XGBoost) was introduced to this study. Yanshan Town in Wanzhou district, Three Gorges reservoir, was chosen as a case study. Nine influencing factors, including engineering geological lithology and thickness of deposit layer, were selected to construct the landslide susceptibility evaluation index system. The relationship between landslide development and these indicators is quantitatively analyzed using the information value model. Subsequently, 70% of landslide samples were randomly assigned for training, while the remaining 30% were used for validation. The XGBoost model was then employed for landslide susceptibility mapping. The output were compared with those of the decision tree model (DT) and gradient boosting decision tree (GBDT) in terms of prediction accuracy and model stability. The findings revealed that distance to the Yangtze River, soil thickness, and lithology were the primary factors influencing landslide development. The XGBoost model demonstrated the highest average prediction accuracy (97.3%) in 100 repeated trials, surpassing the DT (91.3%) and GBDT models. Moreover, the XGBoost model exhibited superior robustness with a standard deviation and coefficient of variation of 0.01, lower than the other two models. It also achieved the highest accuracy (94.3%) and prediction accuracy (97.3%) in the validation process. The proposed XGBoost model serves as a reliable assessment method and yields optimal results in regional landslide susceptibility mapping.
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