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地学领域高质量科技期刊分级T2区（2023）

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Volume 43 Issue 3
May  2024
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Article Contents
Article Navigation >  Bulletin of Geological Science and Technology  > 2024  >  43(3): 251-265
GUO Yanhao, DOU Jie, XIANG Zilin, MA Hao, DONG Aonan, LUO Wanqi. Susceptibility evaluation of Wenchuan coseismic landslides by gradient boosting decision tree and random forest based on optimal negative sample sampling strategies[J]. Bulletin of Geological Science and Technology, 2024, 43(3): 251-265. doi: 10.19509/j.cnki.dzkq.tb20230037
Citation: GUO Yanhao, DOU Jie, XIANG Zilin, MA Hao, DONG Aonan, LUO Wanqi. Susceptibility evaluation of Wenchuan coseismic landslides by gradient boosting decision tree and random forest based on optimal negative sample sampling strategies[J]. Bulletin of Geological Science and Technology, 2024, 43(3): 251-265. doi: 10.19509/j.cnki.dzkq.tb20230037
shu

Susceptibility evaluation of Wenchuan coseismic landslides by gradient boosting decision tree and random forest based on optimal negative sample sampling strategies

doi: 10.19509/j.cnki.dzkq.tb20230037

  • Badong National Observation and Research Station of Geohazards, China University of Geosciences(Wuhan), Wuhan 430074, China

More Information
  • Author Bio:

    GUO Yanhao, E-mail: 605431412@cug.edu.cn

  • Corresponding author: DOU Jie, E-mail: doujie@cug.edu.cn
  • Received Date: 28 Jan 2023
  • Accepted Date: 04 May 2023
  • Rev Recd Date: 29 Apr 2023
    Abstract
  • Objective

    Strong earthquake-induced landslides are characterized by large number, wide distribution and large scale, and seriously threaten people's lives and property. Landslide susceptibility mapping (LSM) can quickly predict the spatial distribution of prone areas, which is highly important for reducing the risk of post-earthquake disasters. However, in the studies of coseismic landslide LSMs, how to select negative landslide samples and integrate machine learning models to improve the evaluation accuracy still needs further investigation.

    Methods

    In this study, the landslides induced by the Wenchuan earthquake in mountainous areas are selected as a case study. First, 10 landslide influencing factors, such as topography, geological environment, and seismic parameters, are selected to analyse the spatial distribution of landslides. Then, collinearity analysis is used to test data redundancy, nonnegative sample points from the sampling strategies are randomly selected in the extremely low susceptibility regions by the frequency ratio (FR) method. Finally, gradient boosting decision tree (GBDT), random forest (RF), and their optimal models are used to predict coseismic landslide susceptibility, conduct a comparative study of the models and carry out an accuracy assessment.

    Results

    The results show that ① the spatial distribution of landslides is controlled by multiple factors, and ② the accuracy of the models is FR-RF(AUC=0.943)>FR-GBDT(AUC=0.926)>RF(AUC=0.901)>GBDT(AUC=0.856). ③ Selecting negative landslide samples in low susceptibility areas could significantly improve the accuracy of LSMs.

    Conclusion

    The research results can provide a reference for selecting negative landslide samples and constructing evaluation models, as well as for providing theoretical support for post-earthquake disaster prevention and mitigation.

     

    • The authors declare that no competing interests exist.
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