Assessing shallow landslide susceptibility by using the Generalized Additive Model: A case study

In this work, the Generalized Additive Model (GAM) technique
was implemented for the landslide susceptibility assessment in
the Gravegnola T. basin (Eastern Liguria, Italy), affected by many
shallow landslides caused by the 25 October 2011 rainstorm. Nine
morphological variables, river network, land use and geological
settings were considered in GAM. The predictive performance of
different combinations of these variables (chosen using a stepwise
optimization of the Akaike information criterion) was evaluated
through the cross-validation technique and AUROC computation. A
susceptibility map using all the shallow landslide types was produced
and compared with those obtained by Bartelletti et al. (2017b) for
each different landslide type. The results strengthen the ability of
this methodology to select the most influent predisposing factors.
The bootstrap procedure allowed to compute the 95% probability
confidence intervals of the landslide probability. Their amplitude can
be interpreted as a measure of the spatial model reliability.