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.

Description: The assessment of flow velocity has a central role in quantitative analysis of debris flows, both for the characterization of the phenomenology of these processes, and for the assessment of related hazards. Large scale particle image velocimetry (LSPIV) can contribute to the assessment of surface velocity of debris flows, provided that the specific features of these processes (e.g. fast stage variations and particles up to boulder size on the flow surface) are taken into account. Three debris flow events, each of them consisting of several surges featuring different sediment concentration, flow stage and velocity, have been analyzed at the inlet of a sediment trap in a stream of the eastern Italian Alps (Gadria Creek). Free softwares have been employed for preliminary treatment (ortho-rectification and format conversion) of video-recorded images as well as for LSPIV application. Results show that LSPIV velocities are consistent with manual measurements on the ortho-rectified imagery and with front velocity measured from the hydrographs in a channel reach approximately 70 m upstream of the sediment trap. Horizontal turbulence, computed as the standard deviation of the flow directions at a given cross-section for a given surge, proved to be correlated with surface velocity and with visually estimated sediment concentration. The study demonstrates the effectiveness of LSPIV in the assessment of surface velocity of debris flows, and permit to identify the most crucial aspects for improving the accuracy of debris flows velocity measurements.

Description: Landslides cause severe damage to the road network of the hit zone, in terms of both direct (partial or complete destruction of a road or blockages) and indirect (traffic restriction or the cut-off of a certain area) costs. Thus, the identification of the parts of the road network that are more susceptible to landslides is fundamental to reduce the risk to the population potentially exposed and the financial expense caused by the damage. For these reasons, this paper aimed to develop and test a data-driven model for the identification of road sectors that are susceptible to being hit by shallow landslides triggered in slopes upstream from the infrastructure. This model was based on the Generalized Additive Method, where the function relating predictors and response variable is an empirically fitted smooth function that allows fitting the data in the more likely functional form, considering also non-linear relations. This work also analyzed the importance, on the estimation of the susceptibility, of considering or not the sediment connectivity, which influences the path and the travel distance of the materials mobilized by a slope failure until hitting a potential barrier such as a road. The study was carried out in a catchment of northeastern Oltrepò Pavese (northern Italy), where several shallow landslides affected roads in the last 8 years. The most significant explanatory variables were selected by a random partition of the available dataset in two parts (training and test subsets), 100 times according to a bootstrap procedure. These variables (selected 80 times by the bootstrap procedure) were used to build the final susceptibility model, the accuracy of which was estimated through a 100-fold repetition of the holdout method for regression, based on the training and test sets created through the 100 bootstrap model selection. 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 in which the index of connectivity was also calculated according to a linear relationship, was considered. 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 land use scenarios were considered in order to estimate possible changes in road susceptibility. Land use classes of the study area were characterized by similar connectivity features. As a consequence, variations on the susceptibility of the road network according to different scenarios of distribution of land cover were limited. The results of this research demonstrate the ability of the developed methodology in the assessment of susceptible roads. This could give the managers of infrastructure information about 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.

Description: The assessment of flow velocity has a central role in quantitative analysis of debris flows, both for the characterization of the phenomenology of these processes and for the assessment of related hazards. Large-scale particle image velocimetry (LSPIV) can contribute to the assessment of surface velocity of debris flows, provided that the specific features of these processes (e.g. fast stage variations and particles up to boulder size on the flow surface) are taken into account. Three debris-flow events, each of them consisting of several surges featuring different sediment concentrations, flow stages, and velocities, have been analysed at the inlet of a sediment trap in a stream in the eastern Italian Alps (Gadria Creek). Free software has been employed for preliminary treatment (orthorectification and format conversion) of video-recorded images as well as for LSPIV application. Results show that LSPIV velocities are consistent with manual measurements of the orthorectified imagery and with front velocity measured from the hydrographs in a channel recorded approximately 70 m upstream of the sediment trap. Horizontal turbulence, computed as the standard deviation of the flow directions at a given cross section for a given surge, proved to be correlated with surface velocity and with visually estimated sediment concentration. The study demonstrates the effectiveness of LSPIV in the assessment of surface velocity of debris flows and permit the most crucial aspects to be identified in order to improve the accuracy of debris-flow velocity measurements.