The artificial neural network for the rockfall susceptibility assessment. A case study in Basilicata (Southern Italy)

• Main Authors: Lucia Losasso, Francesco Sdao
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-01-01
• Series: Geomatics, Natural Hazards & Risk
• Subjects: Analysis Neural Networks (ANNs); Basilicata; GIS; Italy; rockfall susceptibility map
• Online Access: http://dx.doi.org/10.1080/19475705.2018.1476413

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.