Applications of GIS techniques for landslide susceptibility assessment at regional scale

The proposed experimental study is aimed at evaluating the landslide susceptibility in Sicily region (Italy), that has an extension larger than 25.000 km2, using a multivariate statistical model as the Logistic Regression. The susceptibility model has been implemented in a GIS environment by using ArcSDM (Arc Spatial Data Modeller) a software extension to ArcMAP-GIS useful for developing spatial prediction models through regional datasets. Landslide locations of the study area were retrieved from the national inventory database; a newly developed algorithm was used to automatically extract the scarp area from the whole landslide polygon. From the many different landslide susceptibility factors which influence landslide occurrence the following five factors were chosen on the basis of detailed analysis of the study area and univariate statistical analysis: slope gradient, lithology, land cover, a curve number derived index and a pluviometric anomaly index. All the regression logistic coefficients and parameters were calculated using a selected landslide training dataset. Through the application of the logistic regression modelling technique the final susceptibility map was derived for the whole area. The results of the analysis were verified using a landslide validation dataset and compared with the probability model. On average, the 81% of the area affected by instability and the 80% of the area not affected by instability was correctly classified by the model.