Analyses of slow-moving landslides interacting with the road network: case studies in Basilicata region (southern Italy)

Road infrastructure plays a key role in the economic development of a society. Thus, ensuring its functionality and safety conditions over time is a crucial and, at the same time, a demanding task that central and local authorities are asked to address. In Italy, road networks often develop within complex geological contexts, where active slow-moving landslides may generate risks to traveling persons and to the roads themselves, the latter being associated with socio-economic impacts. The identification of the road sections most exposed to landslide risk is critical for reducing the population potentially exposed to risk and for minimizing the repair/replacement costs. However, studies specifically oriented to roads affected by existing slow-moving landslides are quite rare in the scientific literature. This is possibly due to different reasons: landslide inventories with reliable information on the past and current state of activity of the phenomena are often not available; assessing the temporal probability of landslides characterized by a given intensity over large areas is not straightforward; the development of large datasets of road displacements and damage through traditional techniques can be time-consuming and sometimes not affordable.

This study proposes a conceptual model aimed at classifying the level of exposure to slow-moving landslide risk of stretches of roads at municipal scale. The activities have been developed in the context of the “Mitigation of natural risks to ensure safety and mobility in mountain areas of Southern Italy” (MitiGO) project.  Adopting a matrix-based approach, the following data are combined: landslide inventories, thematic information, displacement measurements derived from the interferometric processing of synthetic aperture radar images (DInSAR) and damage records obtained from Google Street View. First, a statistical model based on the bivariate correlations between the independent variables (i.e., each significant spatial variable derived from the thematic maps) and the dependent variable (i.e., the slow-moving landslides inventoried in the official map) is applied for zoning the susceptibility to slow-moving landslides at the municipal scale. Then, the information is combined with the level of damage and a monitored rate of movement based on DInSAR-derived ground-displacement measurements along the road network. The output is a correlation matrix combining all the information and classifying each stretch of the road network.

The proposed procedure has been applied to different access routes from a major regional road, the SS407 Basentana highway, to some urban centers of municipalities located in the Basento river basin (Basilicata region, southern Italy).

The analyses carried out at a municipal scale allow the classification of the road stretches potentially exposed to slow-moving landslide risk adopting a fairly simple qualitative ranking procedure, reliable in relation to the scale of analysis, which is based on a few data that are relatively easy to retrieve and to manage. The obtained results can be used to support studies of road networks over large areas aimed at the prioritization of risk-mitigation measures, as well as at the identification of road sections requiring further geomorphological surveys and geotechnical analyses, to be conducted in more detail at a larger scale.