Logistic Regression analysis in the evaluation of mass movements susceptibility: The Aspromonte case study, Calabria, Italy

This work describes the application of Logistic Regression (LR) to an assessment of susceptibility to mass movements in a 850 km 2 study area mainly on the Ionian side of the Aspromonte Range, in southern Calabria. LR is a multivariate function that can be utilised, on the basis of a given set of variables, to calculate the probability that a particular phenomenon (for instance, a landslide) is present. In the present study the set of relevant variables includes: rock type, land use, elevation, slope angle, aspect, slope profile curvature down-slope and across-slope. The aim of this paper is to evaluate the LR performance when the procedure is based on the surveying of mass movements in part of the study area. The procedure adopted was GIS-based, with a 10 m DEM square-grid; for slope and curvature calculation, four adjacent cells were grouped to form a nine-point set for mathematical processing. The LR application consists of four steps: sampling, where all relevant characteristics in a part of the area (ca. 27% of the study zone) are assessed; variable parameterisation, where non-parametric variables are transformed into parametric (or semi-parametric) variables (on at least rank scale); model fitting, where regression coefficients are iteratively calculated in the sample area; model application, where the best-fit regression function is applied to the entire study area. This procedure was applied in two ways: first considering all types, then a single type of mass movement. The ground characteristics of the whole study zone were determined. The LR procedure was first tested by extending the sampling and reclassification steps to the whole study zone to find out the best possible fitting regression; the results of this were then compared with ground truth to maximise performance. Afterwards, the results of LR analysis, based on extension of regression formulas obtained also using 40% sampling zones, were compared with those of the best possible one and ground truth. Comparisons were performed by means of a confusion matrix and a simple correlation between expected vs. observed values for grouped variables. The overall results seem promising: for example, if the 27% sample areas are adopted, 94% of the cells where the probability of the existence of any kind of mass movements is between 85.5% and 95%, are actually affected by mass movements. Results are instead less good when attempting to distinguish between types of mass movement.