From landslide mapping to susceptibility modeling: a test in central Italy

In the framework of the CARG (Geological and Geomorphological Mapping of Italy) project, landslides are also mapped as constituting one of the main surficial layers, masking the bedrock lithologies and related stratigraphic/tectonic contacts. As such, they are frequently mapped with a low resolution both in terms of spatial pattern and typology characterization. In particular, typical landslides affecting slopes in the Italian Apennines (slides and flows) are frequently grouped inside large polygons sometimes at a small catchment scale. However, the possibility to exploit such a reference landslide inventory for landslide susceptibility assessment is of great importance.

In this test, the existing CARG landslide dataset for the “Visso” map (Marche, Italy) was split according to the movement typology by exploiting topography maps and orthophotos, thus producing rotational slides, earth flows, and complex landslides archives (198, 91, and 51 cases, respectively). Multivariate Adaptive Regression Splines (MARS)-based susceptibility models were following prepared by regressing each systematic landslide archive to a specific set of physical-environmental predictors, considered as determining for landslides activation. Furthermore, multicollinearity and variables importance analyses were carried out to verify their relevance and influence in landslide susceptibility assessment. Besides, a new type (LCL_SLU) of slope units, obtained by crossing classic hydrological partitioning with landform classification, was used as mapping units.

The results show good AUC (Area under the ROC curve) for all models when prediction skill is evaluated, with values of 0.82, 0.77, 0.78 for rotational slides, earth flows, and complex landslides, respectively; the same AUC became outstanding when success skill is detected, with 0.91, 0.95, and 0.99 scores, respectively. Finally, for potential use in territorial planning, an integrated map was produced by adding up the single-landslide susceptibility scores and ranking the output on a classical 0-1 scale. The final map reaches an AUC value of 0.89, confirming the high performance of the models.

The results of the test in the “Visso” map suggest as potentially very worth processing the landslide inventories already available from the CARG project to assess landslide susceptibility on a regional to national scale.