Probabilistic Analysis of Shallow Landslide Susceptibility Using Physically Based Model and Fuzzy Point Estimate Method

Abstract

The geomechanical parameters of soils used in physically based model for landslide susceptibility analyses are uncertain due to the inherent uncertainty and variability. In addition, limited sampling is another source of the uncertainty since the input parameters were obtained from very wide study area. Therefore, the analysis of rainfall-induced shallow landslides susceptibility using physically based model requires accounting for the uncertainty. Subsequently, the probability theory has been used to quantify the uncertainty. However, some uncertainties, caused by incomplete information, cannot be managed satisfactorily by probability theory, so fuzzy set theory is more appropriate in the case. In this study, the uncertain parameters in landslide susceptibility analysis were expressed as fuzzy numbers and fuzzy set theory was employed. In order to take into account the fuzzy uncertainties in the evaluation of the probability of failure, point estimate method was applied with fuzzy set theory. This proposed process was performed in GIS based environments since GIS has strong spatial data processing capacity. In order to check the feasibility of the proposed approaches, the proposed methods were applied to a practical example. To evaluate the performance of the model, the results of the landslide susceptibility assessment were compared with the landslide inventories using ROC graph. Based on the results of the practical application, it was concluded that the application of fuzzy set theory shows consistent analysis results and can obtain reasonable results.