Abstract. 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.
Estimation of the susceptibility of a road network to shallow landslides
with the integration of the sediment connectivity