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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: The aim of this paper is to analyze the reactivation mechanism of ancient earth flows, with a view to gleaning information that can subsequently be utilized to formulate a risk-reduction strategy. All considerations made herein are the result of direct experience and observation of actual events which have occurred over the past few decades in the Northern Apennines. Particular attention has been paid to the analysis of the evolution of landslides during actual reactivation, acknowledging a typical, recurring succession of events that precede the failure of the slope. The hazard assessment of these large landslide bodies, which are of slope scale, constitutes a thorny problem, especially in view of the inapplicability of traditional deterministic models such as limit equilibrium stability analysis. Nevertheless, a site-specific assessment of probability of reactivation of these large and ancient earth flows is fundamental to effective land-use planning. [Copyright &y& Elsevier]