首页|Comparative study of different machine learning models in landslide susceptibility assessment:A case study of Conghua District,Guangzhou,China
Comparative study of different machine learning models in landslide susceptibility assessment:A case study of Conghua District,Guangzhou,China
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
国家科技期刊平台
NETL
NSTL
万方数据
Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.
Ao Zhang、Xin-wen Zhao、Xing-yuezi Zhao、Xiao-zhan Zheng、Min Zeng、Xuan Huang、Pan Wu、Tuo Jiang、Shi-chang Wang、Jun He、Yi-yong Li
展开 >
Wuhan Center,China Geological Survey,Ministry of Natural Resources(Geosciences Innovation Center of Central South China),Wuhan 430205,China
Guangzhou Institute of Geological Survey,Guangzhou 510080,China
Hubei Transportation Planning Design Institute Co.,Ltd,Wuhan 430050,China
中国地调局项目中国地调局项目Zhuhai Urban Geological Survey(including informatization)Guangzhou Municipal Bureau of Planning and ResourcesGuangzhou Institute of Geological SurveyGuangzhou Urban Planning Survey and Design Institute
国家科技期刊平台
NSTL
万方数据
