Modelling susceptibility to landsliding in the Umbro-Marchean Apennines, Italy

Massari, Remo (1998) Modelling susceptibility to landsliding in the Umbro-Marchean Apennines, Italy. University of Southampton, Doctoral Thesis.

Abstract

Generalised linear modelling was used to model the relation between landsliding and several independent variables for a small area of the central Apennines in Italy. Raster maps of the landslides and the independent variables were produced from air photographs, topographic and geological maps, and filed survey. Landslides were subdivided by type and degree of activity. Moreover, within each landslide body, rupture and deposit areas were identified, since only the features of the former are relevant when modelling landslide susceptibility. A first set of logistic regression relations were obtained between (i) all landslides (ii) dormant landslide only and (iii) active landslide only, and the independent variables surveyed. The independent variables were chosen to reflect conditions prior to landsliding, in order to obtain a model of the conditions leading to landslides. Particularly, for dormant landslides, properties surveyed at the present time are more likely to reflect post-failure conditions.

The resulting statistical models are very interesting. For this reason, while 17 variables were used for active landslides, only nine were used for all landslides and dormant landslide only. Geology and slope angle were found to be highly significant factors in all models. Slope aspect and strike were also significant, particularly for dormant landslides. For active landslides, vegetation cover and concavity/convexity of the slope were more significant than geology and slope angle. However, the extreme variability of the casual factors, and the diverse influence of each factor in each type of landslide made the single model, while useful in understanding the overall processes occurring in the area, imprecise. Moreover, since more than 50% of the landslides in the area are of the slump & flow type, the model was heavily weighted by this group. To better understand the geomorphic processes connected to landslide occurrence a second set of logistic regressions were obtained for each type of landslide, again subdivided into dormant and active. For the logistic analysis, landslides were separated into eight groups, and each group into active and dormant.

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