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Abstract: This paper conducts a statistical analysis to determine shallow-landslide susceptibility in an approximately 7500-km2 region of the south-eastern Alps (South Tyrol, Italy). The study applies the weight of evidence (WofE) method, which is useful in determining landslide susceptibility in large areas with complex geological and geomorphological settings. The statistical analysis and landslide susceptibility mapping are based on 882 past landslides, three geometric/topographic factors and two anthropogenic factors, which are the most relevant landslide predisposing factors. The quality of the proposed model, particularly the fitting performance, was assessed; the landslide database was divided into a training set to obtain the model and a validation set to estimate the model quality. The results show that the developed susceptibility model predicts an acceptable percentage (75%) of landslides. Therefore, the model can be useful and reliable for land planners and decision makers also due to its cost-effectiveness ratio. [Copyright &y& Elsevier]

Abstract: In this paper we evaluate different methods to predict soil erosion processes. We derived different layers of predictor variables for the study area in the Northern Chianti, Italy, describing the soil–lithologic complex, land use, and topographic characteristics. For a subcatchment of the Orme River, we mapped erosion processes by interpreting aerial photographs and field observations. These were classified as erosional response units (ERU), i.e. spatial areas of homogeneous erosion processes. The ERU were used as the response variable in the soil erosion modelling process. We applied two models i) bootstrap aggregation (Random Forest: RF), and ii) stochastic gradient boosting (TreeNet: TN) to predict the potential spatial distribution of erosion processes for the entire Orme River catchment. The models are statistically evaluated using training data and a set of performance parameters such as the area under the receiver operating characteristic curve (AUC), Cohen''s Kappa, and pseudo R2. Variable importance and response curves provide further insight into controlling factors of erosion. Both models provided good performance in terms of classification and calibration; however, TN outperformed RF. Similar classes such as active and inactive landslides can be discriminated and well interpreted by considering response curves and relative variable importance. The spatial distribution of the predicted erosion susceptibilities generally follows topographic constraints and is similar for both models. Hence, the model-based delineation of ERU on the basis of soil and terrain information is a valuable tool in geomorphology; it provides insights into factors controlling erosion processes and may allow the extrapolation and prediction of erosion processes in unsurveyed areas. [Copyright &y& Elsevier]

Abstract: For the generation of susceptibility maps on medium scales (1:25,000 to 1:50,000) using statistical techniques, a reliable landslide inventory is needed, together with factor maps used as inputs. This paper compares landslide susceptibility maps obtained with the same methodology but using different landslide inventories: the official Italian landslide inventory GeoIFFI for the Lombardy Region and a recently mapped inventory (DF2001). The analysis included four main steps: (i) preparation of debris flow inventories using both random and spatial partitions and factor maps as explanatory variables; (ii) calculation of accountability and reliability indices for a preliminary susceptibility analysis and selection of an appropriate combination of the factor maps for detailed analysis; (iii) evaluation and validation of the obtained susceptibility maps; and (iv) comparison of the results and selection of the final map. The study area is located in the Valtellina Valley in the Central Italian Alps. The analysis identified highly susceptible areas of shallow landslides that may generate debris flows. It was demonstrated that more precisely delimited source areas for landslide-induced debris flows produce better susceptibility maps. However, the improvement of these maps was relatively limited when the inventories were randomly subdivided. Higher improvements were observed after the subdivision of the inventories into three geographical parts with different geomorphological characteristics. Although the modelling showed very similar results if evaluation is made using standard techniques, the spatial pattern of the susceptibility maps was highly variable and dependent on the combination of the factor maps used. [Copyright &y& Elsevier]