Shallow-landslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors

The ''Costa Viola'' mountain ridge (southern Calabria), in the sector between Bagnara Calabra and Scilla, is particularly exposed to geo-hydrological risk conditions. The study area has repeatedly been affected by slope instability events in the last decades, mainly related to debris slides, rock falls and debris flows. These types of slope movements are among the most destructive and dangerous for people and infrastructures, and are characterized by abrupt onset and extremely rapid movements. Susceptibility evaluations to shallow landslides have been performed by only focusing on source activation. A logistic regression approach has been applied to estimating the presence/absence of sources in terms of probability, on the basis of linear statistical relationships with a set of territorial variables. An inventory map of 181 sources, obtained from interpretation of air photographs taken in 1954-1955, has been used as training set, and another map of 81 sources, extracted from 1990 to 1991 photographs, has been adopted for validation purposes. An initial set of 12 territorial variables (i.e. lithology, land use, soil sand percentage, elevation, slope angle, aspect, across-slope and down-slope curvatures, topographic wetness index, distance to road, distance to fault and index of daily rainfall) has been considered. The adopted regression procedure consists of the following steps: (1) parameterization of the independent variables, (2) sampling, (3) calibration, (4) application and (5) evaluation of the forecasting capability. The ''best set'' of variables could be identified by iteratively excluding one variable at a time, and comparing the ROC results. Through a sensitivity analysis, the role of the considered factors in predisposing shallow slope failures in the study area has been evaluated. The results obtained for the Costa Viola mountain ridge can be considered acceptable, as 98.1 % of the cells are correctly classified. According to the susceptibility map, the village of Scilla and its surroundings fall in the highest susceptibility class.