The role of the diagnostic areas in the assessment of landslide susceptibility models: a test in the sicilian chain

Abstract The aim of the research was to verify and compare the predictive power ofdifferent diagnostic areas in assessing landslide susceptibility with a multivariate approach.Scarps, landslide areas (the union between scarp and accumulation zones) and areas uphillfrom crowns, for rotational slides, source or scarp areas and landslide areas, for flows, havebeen tested. A multivariate approach was applied to assess the landslide susceptibility onthe basis of three selected conditioning factors (lithology, slope angle, and topographicwetness index), which were combined in a Unique Condition Unit (UCU) layer. Byintersecting the UCU layer with the vector layer of the diagnostic areas, landslide susceptibilitymodels were produced, in which the susceptibility is assigned to each UCUs onthe basis of the computed density function. In order to test the effects produced byselecting different diagnostic areas in the performance of the susceptibility models, validationprocedures have been applied to evaluate and compare the performances of thederived predictive models. The validation results are estimated by comparing the predictionand the success rate curves, exploiting three morphometric indexes. A test area, theGuddemi river basin, was selected in the northern Sicilian Apennines chain, having a totalarea of nearly 25 km2 and being mainly characterized by the outcropping of clays, calcilutites,and marly limestones. Aerial analysis, integrated with a field survey, resulted inthe recognition of 111 earth-flow and 145 earth-rotational slide landslides. Scarps, forrotational slides, and both source and landslide areas, for flows, produced very satisfactoryvalidation results. For rotational slides, areas uphill from crowns and landslide areas areboth responsible for lower predictive performances, characterized by validation curvesclose to being flat shaped, due to their incapability of identifying specific slope (UCU)conditions. Moreover, because of their limited size, the areas uphill from crowns seem tosuffer from a relevant geostatistical ‘‘instability’’, when a splitting is performed to producethe validation domains, so that an enhanced shift between success and prediction ratecurves is produced. By comparing the relative susceptibility maps, the research allowed usto evaluate the key role played by the selection of the diagnostic areas; the validation of the models is proposed as a tool to quantify such differences in terms of predictiveperformance.