2016
DOI: 10.1007/s11069-016-2443-5
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Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy)

Abstract: 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 registe… Show more

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Cited by 57 publications
(35 citation statements)
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“…However, producing standardized and accurate landslide susceptibility maps at catchment and regional scales is not a straightforward task. Nevertheless, accuracy can be determined with a variety of metrics [118], and the number of algorithms that evaluate accuracy is constantly increasing [119] as technology improves computational efficiency. Numerous modeling techniques have been employed over the past three decades to generate increasingly accurate landslide susceptibility maps, and several landslide researchers are seeking an optimal method or workflow in comparative studies [41,120] and for machine learning algorithms [121].…”
Section: Discussionmentioning
confidence: 99%
“…However, producing standardized and accurate landslide susceptibility maps at catchment and regional scales is not a straightforward task. Nevertheless, accuracy can be determined with a variety of metrics [118], and the number of algorithms that evaluate accuracy is constantly increasing [119] as technology improves computational efficiency. Numerous modeling techniques have been employed over the past three decades to generate increasingly accurate landslide susceptibility maps, and several landslide researchers are seeking an optimal method or workflow in comparative studies [41,120] and for machine learning algorithms [121].…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, a polygon‐to‐point conversion was required for the Wenchuan inventory. In the literature, two conversion methods are available: (i) extracting the highest location along the perimeter of the landslide scar (e.g., Cama et al, ; Lombardo et al, ); or (ii) computing centroids for each polygon (e.g., Hussin et al, ; ZĂȘzere et al, ). Here, we selected the centroid option for consistency between the Lushan and Wenchuan data sets.…”
Section: Data Set Creationmentioning
confidence: 99%
“…Among these, Philips et al (2004Philips et al ( , 2006 presented an integrated approach of maximum entropy and geographic information system (GIS) technologies for species distribution modelling where the application of this algorithm maximizes the entropy in a geographic space. In the present contribution, we exploit the MaxEnt approach to predict the spatial distribution of landslides, with a similar assumption to that described in Convertino et al (2013) and Lombardo et al (2016). Merow et al (2013) illustrate the MaxEnt model architecture as requiring presence-only (PO) data and a set of predictors distributed across a regularly gridded space.…”
Section: Maximum Entropymentioning
confidence: 99%