中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Evaluation and analysis of statistical and coupling models for highway landslide susceptibility

文献类型:期刊论文

AuthorFan, Huadan7; Lu, Yuefeng1,5,6,7; Shao, Shiwei1,4; Li, Li3; Wang, Yanjun5; Lu, Miao1,2; Li, Jing7; Yao, Kaizhong7; Sun, Ying7
SourceGEOMATICS NATURAL HAZARDS & RISK
Issued Date2023-12-31
Volume14Issue:1Pages:23
ISSN1947-5705
KeywordHighway landslide susceptibility statistical model analytic hierarchy process logistic regression ROC curve Sridevi Jadi parameter
DOI10.1080/19475705.2023.2167612
Corresponding AuthorLu, Yuefeng(yflu@sdut.edu.cn) ; Shao, Shiwei(shaoshiwei@163.com) ; Lu, Miao(lumiao@caas.cn)
English AbstractLandslides have a great impact on the normal traffic of highway, and maintaining the normal traffic of highway is the foundation of economic development, so landslide susceptibility mapping is very important. In this study, four counties, which locate in the central Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China, are taken as the research region. Based on the 190 historical landslide disaster points in the region, six factors-elevation, slope, aspect, plan curvature, profile curvature and TWI (Topographic Wetness Index) - are finally selected for calculation. A landslide disaster is evaluated by two single models of CF (Certainty Factors) and IV (Information Value) models and four coupling models of CF-AHP (Analytic Hierarchy Process), CF-LR (Logistic Regression), IV-AHP and IV-LR models. The accuracy of the six models is evaluated by the ROC (Receiver Operating Characteristic) curve and the Sridevi Jadi parameters. The IV-AHP model has the highest value of 0.9189, which indicates that the IV-AHP model is more appropriate for landslide disaster assessment in the whole region. In the Sridevi Jadi parameters, the IV model have the highest value of 0.8696, showing that the IV model have the highest accuracy in landslide susceptibility assessment in high- and very high-susceptibility regions.
WOS KeywordFUZZY MULTICRITERIA
Funding ProjectMajor Project of High Resolution Earth Observation System of China[No.GFZX0404130304] ; Open Fund of Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology[E22201] ; Grant ASTIP[CAAS-ZDRW202201] ; State Key Laboratory of Resources and Environmental Information System ; Innovation Capability Improvement Project of Scientific and Technological Small and Medium-sized Enterprises in Shandong Province of China[2021TSGC1056]
WOS Research AreaGeology ; Meteorology & Atmospheric Sciences ; Water Resources
Language英语
PublisherTAYLOR & FRANCIS LTD
WOS IDWOS:000919681700001
Funding OrganizationMajor Project of High Resolution Earth Observation System of China ; Open Fund of Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology ; Grant ASTIP ; State Key Laboratory of Resources and Environmental Information System ; Innovation Capability Improvement Project of Scientific and Technological Small and Medium-sized Enterprises in Shandong Province of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/189438]  
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLu, Yuefeng; Shao, Shiwei; Lu, Miao
Affiliation1.Hunan Univ Sci & Technol, Natl Local Joint Engn Lab Geospatial Informat Tech, Xiangtan, Hunan, Peoples R China
2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing, Peoples R China
3.China Transport Telecommun & Informat Ctr, Beijing, Peoples R China
4.Zhongzhi Software Technol Co Ltd, Luoyang, Henan, Peoples R China
5.Hunan Univ Sci & Technol, Hunan Prov Key Lab Geoinformat Engn Surveying, Mapping & Remote Sensing, Xiangtan, Hunan, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
7.Shandong Univ Technol, Sch Civil & Architectural Engn, Zibo, Shandong, Peoples R China
Recommended Citation
GB/T 7714
Fan, Huadan,Lu, Yuefeng,Shao, Shiwei,et al. Evaluation and analysis of statistical and coupling models for highway landslide susceptibility[J]. GEOMATICS NATURAL HAZARDS & RISK,2023,14(1):23.
APA Fan, Huadan.,Lu, Yuefeng.,Shao, Shiwei.,Li, Li.,Wang, Yanjun.,...&Sun, Ying.(2023).Evaluation and analysis of statistical and coupling models for highway landslide susceptibility.GEOMATICS NATURAL HAZARDS & RISK,14(1),23.
MLA Fan, Huadan,et al."Evaluation and analysis of statistical and coupling models for highway landslide susceptibility".GEOMATICS NATURAL HAZARDS & RISK 14.1(2023):23.

入库方式: OAI收割

来源:地理科学与资源研究所

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