In this work an overview of the potential rock fall source areas and propagation assessment in the Province of Potenza territory has been presented. The rock fall process is characterized by two steps: the detachment of blocks and subsequently their propagation along the slope. The adopted methodology, used for the first time in the study area, and the software Histofit and FlowR have been very useful tools for the preliminary assessment of rock fall susceptibility at a regional scale, in particular because they have required low data of the study area. Only the DEM may be sufficient together with an appropriate choice of the input parameters and algorithms, that is to say: calculation method, directions algorithm, inertial algorithm and friction loss function. The output of the model is a map of the rock fall source areas, the propagation probabilities and the propagation kinetic energy. The results show that the adopted methodology is successful for the identification of rock fall source areas at a regional scale and the propagation probability obtaining an interesting rock fall susceptibility map
This paper presents the results obtained by the elaboration of an artificial neuronal network for the creation of a rockfall susceptibility map. The analysis was carried out by analysing the predisposing and triggering factors of the rockfall phenomenon. The parameters considered for this study and representing the input data of the artificial neural network are factors such as: gradient, soil use, lithology, rockfall source areas and kinetic energy values obtained by considering the probable pathways of the blocks through simulations with dedicated softwares, DEMs and niches of the rockfalls that have already occurred in the past. The processing of this data (required in a versatile dedicated software for the realization of the artificial neural network in ASCII format) is done using GIS softwares, useful tools for the creation of hazard maps. An important step is the realization of the rockfall inventory map: it allows to identify the training set (consisting of 50% of the pixels relative to the rockfall niches) for the network training and the testing set (considering the remaining 50% of the pixels relative to the rockfall niches) to assess the network accuracy by overlaying the rockfall niches belonging to the testing set with the obtained susceptibility map.
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