ALBERT

Description: Landslides causes severe damages to the road network of a hit zone, in terms of both direct (partial or complete destruction of a road trait, blockages) and indirect (traffic restriction, cut-off of a certain area) costs. Thus, the identification of the parts of the road network which are more susceptible to landslides is fundamental to reduce the risk to the population potentially exposed and the money expense caused by road damaging. For these reasons, this paper aimed to develop and test a data-driven model based on the Genetic Algorithm Method for the identification of road sectors that are susceptible to be hit by shallow landslides triggered in slopes upstream to the infrastructure. This work also analyzed the importance of considering or not the sediment connectivity on the estimation of the susceptibility. The study was carried out in a catchment of north-eastern Oltrepò Pavese (northern Italy), where several shallow landslides affected roads in the last 8 years. The random partition of the dataset used for building the model in two parts (training and test subsets), within a 100-fold bootstrap procedure, allowed to select the most significant explanatory variables, providing a better description of the occurrence and distribution of the road sectors potentially susceptible to damages induced by shallow landslides. The presented methodology allows the identification, in a robust and reliable way, of the most susceptible road sectors that could be hit by sediments delivered by landslides. The best predictive capability was obtained using a model which took into account also the index of connectivity, calculated according to a linear relationship. Most susceptible road traits resulted to be located below steep slopes with a limited height (lower than 50 m), where sediment connectivity is high. Different scenarios of land use were implemented in order to estimate possible changes in road susceptibility. Land use classes of the study area were characterized by similar connectivity features with a consequent loss of variations also on the susceptibility of the road networks according to different scenarios of distribution of land cover. Larger effects on sediment connectivity and, as a consequence on road susceptibility, could be due to modifications in the morphology of the slopes (e.g. drainage system, modification of the slope angle) caused by the abandonment or by the recovery of cultivations. The results of this research demonstrate the ability of the developed methodology in the assessment of susceptible roads. This could give to the managers of an infrastructure information on the criticality of the different road traits, thereby allowing attention and economic budgets to be shifted towards the most critical assets, where structural and non-structural mitigation measures could be implemented.