Comparative study of different machine learning models in landslide susceptibility assessment:A case study of Conghua District,Guangzhou,China
Ao Zhang Jun He Yi-yong Li Xin-wen Zhao Xing-yuezi Zhao Xiao-zhan Zheng Min Zeng Xuan Huang Pan Wu Tuo Jiang Shi-chang Wang
中国地质（英文）2024，Vol.7Issue(1)：104-115,12.
中国地质（英文）2024，Vol.7Issue(1)：104-115,12.DOI:10.31035/cg2023056
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
摘要
关键词
Landslides susceptibility assessment/Machine learning/Logistic Regression/Random Forest/Support Vector Machines/XGBoost/Assessment model/Geological disaster investigation and prevention engineeringKey words
Landslides susceptibility assessment/Machine learning/Logistic Regression/Random Forest/Support Vector Machines/XGBoost/Assessment model/Geological disaster investigation and prevention engineering引用本文复制引用
Ao Zhang,Jun He,Yi-yong Li,Xin-wen Zhao,Xing-yuezi Zhao,Xiao-zhan Zheng,Min Zeng,Xuan Huang,Pan Wu,Tuo Jiang,Shi-chang Wang..Comparative study of different machine learning models in landslide susceptibility assessment:A case study of Conghua District,Guangzhou,China[J].中国地质（英文）,2024,7(1):104-115,12.基金项目
This research was supported by the projects of the China Geological Survey(DD20221729,DD20190291)and Zhuhai Urban Geological Survey(including informatization)(MZCD-2201-008).The authors are indebted to Guangzhou Municipal Bureau of Planning and Resources,Guangzhou Institute of Geological Survey,Guangzhou Urban Planning Survey and Design Institute for their assistance.The authors are also thankful to the reviewers and editors for their valuable comments and suggestions. （DD20221729,DD20190291）
