A Comparison between Heuristic, Statistical and Data-driven Methods in Landslide Susceptibility Assessment: an Application to the Briga and Giampilieri Catchments

Abstract

Susceptibility assessment concerning the estimation of areas prone to landslide is one of the most useful approach in the analysis of landslide hazard. Over the last years, in an attempt to find the best approach to evaluate landslide susceptibility, many methods have been developed. Among these, the heuristic, the statistical, and the data-driven approaches are very widespread, and they all are based on the concept that the conditions which led to landslide movements in the past will control the probability of movement occurrence in the future. This study presents an assessment of landslide susceptibility in which models of the three different methodologies, such as the heuristic approach, the logistic regression, which belongs to the generalized linear models, and the artificial neural networks are used along with GIS spatial analysis techniques. We compare the results by applying the three different approaches to evaluate the debris-mud flows susceptibility to Briga and Giampilieri basins, two catchments of the city area of Messina (Sicily) where a considerable number of historical events were documented. The evaluation is carried out by comparing the AUC curves resulting from the application of the three approaches.