大地测量与地球动力学2026，Vol.46Issue(1)：68-77,10.DOI:10.14075/j.jgg.2024.12.596
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
Landslide Susceptibility Distribution in Anhui Province Based on Machine Learning Model and Feature Selection
摘要
Abstract
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.关键词
滑坡/机器学习/特征筛选/易发性/集成模型Key words
landslide/machine learning/feature selection/susceptibility/ensemble models分类
天文与地球科学引用本文复制引用
陆子康,陈宏信,冯世进,赵勇,沈国栋,陈修和..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[J].大地测量与地球动力学,2026,46(1):68-77,10.基金项目
长三角科技创新共同体联合攻关项目(2022CSJGG1202). （2022CSJGG1202）
