Evaluation and analysis of statistical and coupling models for highway landslide susceptibility

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.

Keywords:

• Highway landslide susceptibility
• statistical model
• analytic hierarchy process
• logistic regression
• ROC curve
• Sridevi Jadi parameter

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from [Resource and Environment Science and Data Center, Geospatial Data Cloud]. Restrictions apply to the availability of these data, which were used under license for this study. Data are available [at https://www.resdc.cn/, http://www.gscloud.cn/] with the permission of [Resource and Environment Science and Data Center, Geospatial Data Cloud].

Additional information

Funding

This work was supported by the Major Project of High Resolution Earth Observation System of China under Grant No.GFZX0404130304; the Open Fund of Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology under Grant No.E22201; the Agricultural Science and Technology Innovation Program under Grant ASTIP No. CAAS-ZDRW202201; a grant from State Key Laboratory of Resources and Environmental Information System; and the Innovation Capability Improvement Project of Scientific and Technological Small and Medium-sized Enterprises in Shandong Province of China under Grant No.2021TSGC1056.