000			01948nam a22002417a 4500
003			ZW-GwMSU
005			20221129123055.0
008			221129b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100	1		_aManzo, G _eauthor
245	1	0	_aGIS techniques for regional-scale landslide susceptibility assessment: the Sicily (Italy) case study _ccreated by G. Manzo, V. Tofani,S. Segoni ,A. Battistini &F. Catani
264			_aFirenze _bTaylor & Francis _c2013
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aThis study describes the assessment of landslide susceptibility in Sicily (Italy) at a 1:100,000 scale using a multivariate logistic regression model. The model was implemented in a GIS environment by using the ArcSDM (Arc Spatial Data Modeller) module, modified to develop spatial prediction through regional data sets. A newly developed algorithm was used to automatically extract the detachment area from mapped landslide polygons. The following factors were selected as independent variables of the logistic regression model: slope gradient, lithology, land cover, a curve number derived index and a pluviometric anomaly index. The above-described configuration has been verified to be the best one among others employing from three to eight factors. All the regression coefficients and parameters were calculated using selected landslide training data sets. The results of the analysis were validated using an independent landslide data set. On an average, 82% of the area affected by instability and 79% of the not affected area were correctly classified by the model, which proved to be a useful tool for planners and decision-makers.
650			_alandslide susceptibility
650			_alogistic regression
650			_aGIS
856			_uhttps://doi.org/10.1080/13658816.2012.693614
942			_2lcc _cJA
999			_c160662 _d160662