The influence of deep-seated gravitational slope deformations (DSGSDs) on the distribution of translational landslides was analyzed in the Milia basin, Tuscany, Italy. Detailed geomorphological mapping, combined with the analysis of aerial photography, enabled us to build two landslide inventories. One inventory including landslides before 1975 was used to create statistical models, whereas the other inventory including landslides after 1975 was used to validate the models. Geology, slope angle, slope aspect, distance to hydrographic elements, and distance to tectonic lineaments were considered as landslide-predisposing factors. To quantify the importance of DSGSDs as another landslide-predisposing factor, the DSGSD presence/absence map was introduced in the stepwise statistical analysis. The landslide inventory maps and factormaps were processed using a conditional analysis on all possible factor combinations to produce landslide susceptibilitymaps with five susceptibility classes. The results indicate that the DSGSDs significantly affect landslide distribution.

Influence of deep-seated gravitational slope deformations on landslide distributions: A statistical approach

RIBOLINI, ADRIANO;
2013

Abstract

The influence of deep-seated gravitational slope deformations (DSGSDs) on the distribution of translational landslides was analyzed in the Milia basin, Tuscany, Italy. Detailed geomorphological mapping, combined with the analysis of aerial photography, enabled us to build two landslide inventories. One inventory including landslides before 1975 was used to create statistical models, whereas the other inventory including landslides after 1975 was used to validate the models. Geology, slope angle, slope aspect, distance to hydrographic elements, and distance to tectonic lineaments were considered as landslide-predisposing factors. To quantify the importance of DSGSDs as another landslide-predisposing factor, the DSGSD presence/absence map was introduced in the stepwise statistical analysis. The landslide inventory maps and factormaps were processed using a conditional analysis on all possible factor combinations to produce landslide susceptibilitymaps with five susceptibility classes. The results indicate that the DSGSDs significantly affect landslide distribution.
M., Capitani; Ribolini, Adriano; P. R., Federici
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/235648
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