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

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.