Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy)

Clicks: 176
ID: 261745
2016
This study evaluates the performances of the presence-only approach, Maximum Entropy, in assessing landslide triggering-thickness susceptibility within the Mili catchment (Sicily, Italy). This catchment underwent several meteorological stresses, resulting in hundreds of shallow rapid mass movements between 2007 and 2011. In particular, the area has become known for two disasters, which occurred in 2009 and 2010; the first weather system did not pass directly over the catchment; however, peak rainfall was registered over the basin during the second meteorological event. Field data were collected to associate the depth from the slope surface that material was mobilised at the triggering zone to each mass movement within the catchment. This information has been used to model the landslide susceptibility for two classes of processes, divided into shallow failures for maximum depths of 1 m and deep ones in case of values equal or greater than 1 m. Topographic attributes from a 2-m DEM were used as predictors, together with medium resolution vegetation indexes derived from ASTER scenes and geological, land use and tectonic maps. The presence-only approach discriminated between the two depth classes at the landslide trigger zone, producing excellent prediction skills associated with relatively low variances across a set of 50 randomly generated replicates. The role of each predictor was assessed to ascertain the significance to the final model output. This work uses simple field measurements to produce triggering-thickness susceptibility, which is a novel approach and may perform better as a proxy for landslide hazard assessments with respect to more common susceptibility practises.
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lombardo2016naturalpresence-only Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors L. Lombardo;G. Fubelli;G. Amato;M. Bonasera;L. Lombardo;G. Fubelli;G. Amato;M. Bonasera;
Journal Natural Hazards
Year 2016
DOI doi:10.1007/s11069-016-2443-5
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