Comparing Forward Conditional Analysis and Forward Logistic Regression Methods in a Landslide Susceptibility Assessment: A Case Study in Sicily
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Materia
Landslide susceptibility assessment Forward logistic regression Forward conditional analysis GIS Sicily
Date
2020-07-10Abstract
Forward logistic regression and conditional analysis have been compared to assess landslide
susceptibility across the whole territory of the Sicilian region (about 25,000 km2) using previously
existing data and a nested tiered approach. These approaches were aimed at singling out a statistical
correlation between the spatial distribution of landslides that have a ected the Sicilian region in the
past, and a set of controlling factors: outcropping lithology, rainfall, landform classification, soil use,
and steepness. The landslide inventory used the proposal of building the models like the o cial
one obtained in the PAI (hydro geologic asset plan) project, amounting to more than 33,000 events.
The 11 types featured in PAI were grouped into 4 macro-typologies, depending on the inherent
conditions believed to generate various kinds of failures and their kinematic evolution. The study has
confirmed that it is possible to carry out a regional landslide susceptibility assessment based solely
on existing data (i.e., factor maps and the landslide archive), saving a considerable amount of time
and money. For scarp landslides, where the selected factors (steepness, landform classification, and
lithology) are more discriminate, models show excellent performance: areas under receiver operating
characteristic (ROC) (AUCs) average > 0.9, while hillslope landslide results are highly satisfactory
(average AUCs of about 0.8). The stochastic approach makes it possible to classify the Sicilian territory
depending on its propensity to landslides in order to identify those municipalities which are most
susceptible at this level of study, and are potentially worthy of more specific studies, as required by
European-level protocols.
Keywords: landslide susceptibility