Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions
Citation
Cui, H., Medina, V., Hürlimann, M., Ji, J. (2024). Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions. Computers and Geotechnics, 172: 106400. Link to paper
Abstract
The inherent uncertainty in hydro-geotechnical parameters presents a significant challenge for accurately predicting rainfall-triggered shallow landslides in mountainous regions. In this study, a novel probabilistic framework was developed and implemented in the Py.GIS-FSLAM-FORM software, designed to address the complexities associated with parameter uncertainty, correlation, and distribution. By combining the Fast Shallow Landslide Assessment Model (FSLAM) with the First-Order Reliability Method (FORM), we have enhanced the traditional probabilistic approach to create more accurate landslide susceptibility maps. The model considers different hydrological conditions and geotechnical uncertainties at the regional scale, providing a fast and efficient tool for landslide susceptibility assessment. This framework enables rapid regional-scale analysis while maintaining the physical basis of slope stability evaluation, making it particularly valuable for large-area landslide risk assessment and early warning systems.