A GIS-based tool for probabilistic physical modelling and prediction of landslides: GIS-FORM landslide susceptibility analysis in seismic areas

• Published in: Landslides Vol. 19; no. 9; pp. 2213 - 2231
• Main Authors: Ji, Jian, Cui, Hongzhi, Zhang, Tong, Song, Jian, Gao, Yufeng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022; Springer Nature B.V
• Subjects: Agriculture; Algorithms; Analysis; Civil Engineering; Earth and Environmental Science; Earth Sciences; Earthquake prediction; Earthquakes; Empirical analysis; Environmental risk; Geographic information systems; Geographical information systems; Geography; Geological hazards; Geological mapping; Geology; Information systems; Landslides; Landslides & mudslides; Mathematical models; Methods; Modelling; Natural Hazards; Parameters; Predictions; Probability theory; Programming languages; Reliability; Reliability analysis; Reliability aspects; Remote sensing; Risk management; Safety factors; Seismic activity; Seismic response; Slopes; Statistical analysis; Susceptibility; Technical Note; Uncertainty; Seismic slope stability; Landslides; GIS toolbox; Probabilistic modelling; First order reliability method (FORM)
• ISSN: 1612-510X; 1612-5118
• DOI: 10.1007/s10346-022-01885-9

Landslide is regarded as one of the most prevalent and destroying geological hazards in natural terrain areas. Reliable landslide susceptibility analysis procedures are vital for policymakers to manage the regional-scale landslide risk. In the framework of physically based modelling analysis, the infinite slope model is commonly used to assess the surficial landslide susceptibility with deterministically defined geotechnical and geological parameters. This work aims to develop a user-friendly geographic information system (GIS) extension tool called the GIS-FORM landslide prediction toolbox using the Python programming language to consider the possible uncertainties in the physically based landslide susceptibility analysis in seismic areas. We implement the first-order reliability method (FORM) algorithm to calculate the probability of infinite slope failures. The proposed toolbox can produce some regional hazard distribution maps of different indexes, such as the factor of safety (FoS), reliability index (RI), and failure probability (P f ). Furthermore, the toolbox enables coseismic landslide displacement prediction using either the direct Newmark integration method and/or the empirical formula method. Outputs of the GIS-FORM landslide prediction analysis are verified using published data in the literature. Further, it is also successfully employed for landslide susceptibility analysis of the Ms 7.0 Jiuzhaigou earthquake in Sichuan Province, China. Without loss of generality, the GIS-FORM landslide prediction toolbox can serve for the rapid hazard mapping of earthquake-induced regional landslides where uncertainties in geological and geotechnical parameters should be considered.