We evaluated the landslide susceptibility in Sicily region (Italy) (25,000 km2) using a multivariate Logistic Regression model. The susceptibility model was implemented in a GIS environment by using ArcSDM (Arc Spatial Data Modeller) to develop spatial prediction models through regional datasets. A newly developed algorithm was used to automatically extract the scarp area from the whole landslide polygon. From the many susceptibility factors which influence landslide occurrence, on the basis of detailed analysis of the study area and univariate statistical analysis, the following factors were chosen: slope gradient, lithology, land cover, a curve number derived index and a pluviometric anomaly index. All the regression logistic coefficients and parameters were calculated using a selected landslide training dataset. Through the application of the logistic regression modelling technique the final susceptibility map was derived for the whole area. The results of the analysis were validated using an independent landslide dataset. On average, the 81 % of the area affected by instability and the 80 % of the area not affected by instability was correctly classified by the model.

Application of GIS techniques for landslide susceptibility assessment at regional scale / Manzo G.; Tofani V.; Segoni S.; Battistini A.; Catani F.. - STAMPA. - (2013), pp. 459-465. [10.1007/978-3-642-31325-7_59]

Application of GIS techniques for landslide susceptibility assessment at regional scale

MANZO, GOFFREDO;TOFANI, VERONICA;SEGONI, SAMUELE;BATTISTINI, ALESSANDRO;CATANI, FILIPPO
2013

Abstract

We evaluated the landslide susceptibility in Sicily region (Italy) (25,000 km2) using a multivariate Logistic Regression model. The susceptibility model was implemented in a GIS environment by using ArcSDM (Arc Spatial Data Modeller) to develop spatial prediction models through regional datasets. A newly developed algorithm was used to automatically extract the scarp area from the whole landslide polygon. From the many susceptibility factors which influence landslide occurrence, on the basis of detailed analysis of the study area and univariate statistical analysis, the following factors were chosen: slope gradient, lithology, land cover, a curve number derived index and a pluviometric anomaly index. All the regression logistic coefficients and parameters were calculated using a selected landslide training dataset. Through the application of the logistic regression modelling technique the final susceptibility map was derived for the whole area. The results of the analysis were validated using an independent landslide dataset. On average, the 81 % of the area affected by instability and the 80 % of the area not affected by instability was correctly classified by the model.
9783642313240
Landslide Science and Practice - Volume 1: Landslide Inventory and Susceptibility and Hazard Zoning
459
465
Manzo G.; Tofani V.; Segoni S.; Battistini A.; Catani F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/821940
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