Abstract
This work consists of the application of a modified method for the landslide risk assessment along a strategic touristic road. The proposed qualitative method is a modification of the original method Rockfall Hazard Rating System (RHRS) developed by Pierson et al. [26] at the Oregon State Highway Division (subsequently modified by other authors) and based on the exponential scoring functions.
The proposed application involves a careful analysis of environmental factors that influence the type of the mass movement as the slope, the use of the soil, the climatic conditions and the lithology, as such as parameters related to the structural characteristics of roads and traffic for example the road width, the number of lanes in each direction and the Average Vehicle Risk. The use of the different technical approaches, like double entry matrices and the implementation, for a few steps, of an Artificial Neural Network (ANN) allows to assess the analyzed landslide intensity, as well as the probability that it will occur in a given area along a transportation corridor. The application of such method involves a first phase of the data retrieval followed by a subsequent implementation and processing in a GIS softwares. The analysis carried out has been characterized by a step aimed at obtaining different layers, essential for the classification of the landsliding predisposing factors cataloged according to a final score by identifying the five risk threshold classes. So, in order to prepare appropriate interventions of protection and monitoring (if necessary, e.g. evacuation plans), underling the most dangerous areas has been fundamental.
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Losasso, L., Rinaldi, C., Alberico, D., Sdao, F. (2017). Landslide Risk Analysis Along Strategic Touristic Roads in Basilicata (Southern Italy) Using the Modified RHRS 2.0 Method. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_55
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