Susceptibility evaluation of Wenchuan coseismic landslides by gradient boosting decision tree and random forest based on optimal negative sample sampling strategies
Susceptibility evaluation of Wenchuan coseismic landslides by gradient boosting decision tree and random forest based on optimal negative sample sampling strategies
[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-earth-quake 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 stud-y.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 redun-dancy,nonnegative sample points from the sampling strategies are randomly selected in the extremely low suscepti-bility regions by the frequency ratio(FR)method.Finally,gradient boosting decision tree(GBDT),random for-est(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 distribu-tion 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 provi-ding theoretical support for post-earthquake disaster prevention and mitigation.
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