Statistical approach and gis techniques in evaluating landslide susceptibility in a sample area of the serchio river basin (italy) - Falaschi, Francesco; Federici, Paolo Roberto; Puccinelli, Alberto; D’amato avanzi, Giacomo; Pochini, Alberto; Spagnolo, Matteo; Bottai, Marco; Salvati, Nicola (Italian journal of engineering geology and environment, 2008)
The aim of this experimental study is to evaluate landslide susceptibility in a sample area of the Serchio River Basin by means of statistical approach and GIS techniques. The study area is characterized by high landslide susceptibility, because of the geological, geomorphological and climatic characteristics of the basin. This area is one of the rainiest in Italy and it is frequently hit by severe rainstorms, which often cause many landslides, involving villages, infrastructures and crops. This study is included in a wider research project, promoted by the Tuscany Region Administration and APAT - Italian Geological Survey, aimed at defining landslide hazard in the area of the Sheet 250 Castelnuovo di Garfagnana (1:50 000 scale). This study is first based on a geomorphologic investigation, in order to realize a landslide inventory map and understand the relationship between geomorphological processes and instability. Eventually, geological and geo-engineering investigations were also performed. Landslide susceptibility assessment proceeded step-by-step. At first, the selection of a certain number of instability factors was performed, according with the geological and geomorphological settings of the study area: geo-engineering characterization of geological formations, slope, aspect, distance from rivers, distance from tectonic elements. Eventually, GIS-supported spatial analysis of landslide distribution related to the presence of instability factors, and statistical analysis, both allowed to better constrain the connections between landslide distribution and instability factors with a quantitative response. The outcome of this study is a classification of the land surface into different susceptibility domains using two distinct statistical methods, both indirect and quantitative.