基于机器学习模型与特征筛选的安徽省滑坡灾害易发性分布研究OA

This paper uses historical landslide and hydrogeological data from Anhui province as the o-riginal dataset,employing five typical machine learning models and selects nine environmental disas-ter-inducing factors as initial input variables.To explore the impact of different machine learning input variable selection combinations on the performance of various models,multiple factor selection meth-ods and several evaluation metrics are used to examine the prediction accuracy of each algorithm model under different selection combinations for landslide susceptibility.The evaluation results show that the combination of the full selection set and the lightweight gradient boosting machine(LightGBM).mod-el yields the best performance.Therefore,this combination is chosen for the landslide susceptibility mapping in the study area.Moreover,the study suggests that factor selection for machine learning in-puts does not necessarily improve model performance in landslide susceptibility studies of the research area.Additionally,ensemble models outperform individual estimator models,and LightGBM,using a leaf-node-based decision tree algorithm,shows improved performance compared to other ensemble al-gorithms.