Difference in Regional Seismic Landslide Risk Prediction Results Based on Different Feature Selection Methods—A Case Study of Wenchuan Earthquake Area

AI Xiao; ZHANG Jian; FU Jimin

doi:10.13512/j.hndz.2024.02.06

Abstract：The regional seismic landslide risk assessment model is a key tool for evaluating the probability and sever-ity of landslides in specific areas when an earthquake occurs.Currently,machine learning-based mathematical modeling methods have become the primary means to construct the assessment model.However,limited research has been conducted on the difference in prediction results of the assessment model caused by the complex and di-verse nature of influencing factors.This study considered 11 influencing factors in the Wenchuan earthquake area and used three feature selection methods,namely correlation coefficient,principal component analysis,and Gini index,to create three types of datasets.Combined with the artificial neural network model,seismic landslide risk assessment models for the Wenchuan earthquake area were constructed based on different datasets obtained by the above three methods and the difference in the prediction results was meticulously analyzed.The results indicate that the assessment model based on the datasets obtained by the principal component analysis method achieves the high-est accuracy in identifying areas with a very high risk level.In addition,it demonstrates a frequency ratio accuracy of 92%and a prediction accuracy of the receiver operating characteristic(ROC)curve of 93.3%.Therefore,it ex-hibits the highest prediction accuracy among the three groups of assessment models.This research aims to provide valuable insights for researchers involved in the construction of seismic landslide risk assessment models.Addition-ally,it provides a theoretical basis for developing a universal feature selection method that integrates multidimen-sional datasets from multiple seismic regions and sets of features.