Neural Network model for predicting landslide susceptibility: a case study from Crotone Province (Calabria, South Italy)

Landsliding is a natural geologic and geomorphic process that plays a key role in denudation, landform development, in hilly-mountainous terrain. Besides, landslides are one of the most natural hazard that cause damage to both property and life all over the world. During the last decades, the landslide hazard assessment has become a subject of major interest because it plays a major role in land use planning. Owing to its particular geological and geomorphologic setting, Italy is a country widely affected by landslides. In Calabria, landslides are very frequent all over the regional territory. This paper addresses the issue of spatial prediction known as landslide susceptibility expressed as the spatial correlation between factors leading to the initiation of the slope failures and the distribution of landslides in the territory. There are a number of methods to obtain landslide susceptibility maps of which the evaluation of the susceptibility demands correct identification and quantitative assessment of the conditioning factors. All the methods are based on the criterion that ‘‘slope-failure in the future will be more likely to occur under those conditions which led to past and present instability’’ (CARRARA et alii, 1995). Accordingly, the susceptibility assessment can be used to predict the spatial location of future failures in territories with similar geo-environmental conditions. In particular, in this work the artificial neural network (ANN) technique was tested for the evaluation and mapping the landslide susceptibility in Crotone Province (Calabria, South Italy).