Assessment of rainfall-induced shallow landslide susceptibility using a GIS-based probabilistic approach

Abstract

This study proposes a probabilistic analysis method to assess shallow landslide susceptibility over an extensive area by integrating an infinite slope model with GIS (Geographic Information System) and Monte Carlo simulation, taking into consideration the inherent uncertainty and variability in input parameters. The mechanical parameters of soil materials (such as cohesion and friction angle) used in the infinite slope analysis have been identified as the major source of uncertainty because of their spatial variability; therefore, these parameters were considered as random variables in this probabilistic landslide analysis. To properly account for the uncertainty in input parameters, the probabilistic analysis method used was Monte Carlo simulation. The process was carried out in a GIS-based environment because GIS has effective spatial data-processing capacity over broad areas. In addition, the hydrogeological model was coupled with the infinite slope model to evaluate increases in pore water pressure caused by rainfall.The proposed approach was applied to a practical example to evaluate its feasibility. The landslide inventory map and the spatial database for input parameters were constructed in a grid-based GIS environment and a probabilistic analysis was implemented using Monte Carlo simulation. To evaluate the performance of the model, the results of the probabilistic landslide susceptibility analysis were compared with the landslide inventory. The probabilistic approach demonstrated good predictive performance when compared with the landslide occurrence location. In addition, deterministic analysis was carried out using fixed single-input data for comparison with the results from the proposed approach. In this comparison, the probabilistic analysis showed better performance than the deterministic analysis. In addition, the results showed that proper consideration and understanding of uncertainties play an important role in accurately predicting shallow landslide susceptibility.