A GIS-based tool for probabilistic physical modelling and prediction of landslides: GIS-FORM landslide susceptibility analysis in seismic areas
Citation
Ji, J., Cui, H., Zhang, T., Song, J., Gao, Y. (2022). A GIS-based tool for probabilistic physical modelling and prediction of landslides: GIS-FORM landslide susceptibility analysis in seismic areas. Landslides, 19(9): 2213-2231. Link to paper
Abstract
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. 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 models. The tool focuses on landslide susceptibility analysis in seismic areas and integrates the First Order Reliability Method (FORM) for probabilistic analysis of slope stability. The GIS-FORM toolbox provides a practical solution for conducting regional-scale landslide susceptibility assessments that account for both the physical mechanisms of slope failure and the inherent uncertainties in geotechnical parameters. The tool has been validated through case studies in seismically active regions, demonstrating its effectiveness in producing reliable landslide susceptibility maps that can support decision-making in landslide risk management and land-use planning.