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

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.