Evaluation and analysis of statistical and coupling models for highway landslide susceptibility. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Fan, Huadan; Lu, Yuefeng; Shao, Shiwei; Li, Li; Wang, Yanjun; Lu, Miao; Li, Jing; Yao, Kaizhong; Sun, Ying
• Is Part Of: Geomatics, natural hazards & risk. Volume 14:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Highway landslide susceptibility -- statistical model -- analytic hierarchy process -- logistic regression -- ROC curve -- Sridevi Jadi parameter; Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2023.2167612
• Languages: English
• ISSNs: 1947-5705

Abstract

Abstract: Landslides 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.