Landslide susceptibility evaluation method considering spatial heteroge-neity and feature selection
The establishment of an accurate,reliable and efficient landslide susceptibility assessment method is a key tool for pre-disaster scientific warning and comprehensive prevention and control.However,the traditional landslide susceptibility eval-uation method fails to effectively address the prediction bias caused by the spatial heterogeneity and redundant features.To address this problem,this paper proposes a method for evaluating landslide susceptibility(SF-Stacking)that takes into account spatial heterogeneity and feature optimization.The method first uses AGNES(agglomerative nesting)to divide the global raster cells into several local regions,then uses a strategy which takes into account feature optimization to select the optimal combina-tion of feature factors for each sub-region,and finally uses Stacking integration technology to couple multiple machine learning algorithms to achieve landslide susceptibility evaluation.Using Yibin city as the study area,the SF-Stacking method is com-pared with seven state-of-the-art methods based on the landslide hazard susceptibility zoning map and statistical indicators.Re-sults show that the SF-Stacking method has the best accuracy,the highest robustness and the best interpretability.
NETL
NSTL
万方数据
