Analysis of the Predisposing Factors for Different Landslide Types Using the Generalized Additive Model

Abstract

In this paper, a semi-parametric nonlinear regression technique, known as Generalized Additive Model (GAM), was implemented for the landslide susceptibility assessment in the Gravegnola catchment (Northern Apennines, Eastern Liguria, Italy), which was affected by more than 500 shallow landslides on the 25 October 2011 intense rainfall event. Twelve explanatory variables derived from DEM with 5-m resolution, river network, land use and geological maps were considered to investigate their influence on landslide type occurrence. The predictive performance of different combinations of explanatory variables has been evaluated through a cross-validation technique and ROC curve analysis. Different susceptibility maps for each landslide type were finally produced and the results were compared. The preliminary results show the higher ability of GAM than a single regression technique in selecting the most influent predisposing factors on the basis of the type of movement involved in landsliding.