Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain)

Abstract

Abstract.A procedure to select the controlling factors connected to the slope instability has been defined. It allowed us to assess the landslide susceptibility in the Rio Beiro basin (about 10 km 2 ) over the northeastern area of the city of Granada (Spain). Field and remote (Google EarthTM) recognition techniques allowed us to generate a landslide inventory consisting in 127 phenomena. To discriminate between stable and unstable conditions, a diagnostic area had been chosen as the one limited to the crown and the toe of the scarp of the landslide. 15 controlling or determining factors have been defined considering topographic, geologic, geomorphologic and pedologic available data. Univariate tests, using both association coefficients and validation results of singlevariable susceptibility models, allowed us to select the best predictors, which were combined for the unique conditions analysis. For each of the five recognised landslide typologies, susceptibility maps for the best models were prepared. In order to verify both the goodness of fit and the prediction skill of the susceptibility models, two different validation procedures were applied and compared. Both procedures are based on a random partition of the landslide archive for producing a test and a training subset. The first method is based on the analysis of the shape of the success and prediction rate curves, which are quantitatively analysed exploiting two morphometric indexes. The second method is based on the analysis of the degree of fit, by considering the relative error between the intersected target landslides by each of the different susceptibility classes in which the study area was partitioned. Both the validation procedures confirmed a very good predictive performance of the susceptibility models and of the actual procedure followed to select the controlling factors.

Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results

“…It is considered a predisposing factor and has been used for landslide probability of occurrence mapping (Varnes et al, 1984;Costanzo et al, 2012;Catani et al, 2013). The original Corine data set (Bossard et al, 2000) produced at scale 1 : 10 000 has 45 classes.…”

“…This layer has been used by Costanzo et al (2012) for LSM on a large scale, resulting as an effective factor for translational slides.…”

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

“…It is closely followed by Sediment transport index (IG = 0.11) and the stream power index (IG = 0.06). It is reasonable because the slope is considered as the most important factors in landslide modeling [78][79][80]. The aspect reveals a high predictive ability because in this study 82.8% of the landslide pixels are occurred in south, southeast, and southwest facing slopes [46].…”

Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

“…Examples of previous studies Conditional analysis (CA) Clerici et al (2010, Conoscenti et al (2008, Costanzo et al (2012a, 2012b, Irigaray et al (2007), Jiménez-Peralvárez et al (2009), Rotigliano et al (2011, 2012, Vergari et al (2011) Discriminant analysis (DA) Baeza and Corominas (2001), Carrara (1983), Carrara et al (2008), Guzzetti et al (2006), Rossi et al (2010. ) Binary logistic regression (BLR) Atkinson and Massari (1998), Ayalew and Yamagishi (2005), Bai et al (2010), Can et al (2005), Carrara et al (2008), Chauan et al (2010), Conforti et al (2012), Dai and Lee (2002), Davis and Ohlmacher (2002), Erener and Düzgün (2010), Mathew et al (2009), Nandi and Shakoor (2009), Nefeslioglu et al (2008, Ohlmacher and Davis (2003), Van den Eckhaut et al (2006 Classification and regression trees (CART) Felicísimo et al (2012), Vorpahl et al (2012) Artificial neuronal networks (ANN) Aleotti and Chowdhury (1999), Ermini et al (2005), Lee et al (2004), Pradhan and Lee (2010) Original Paper exploited to compare the fitting of the model having only the constant term (all the β p are set to 0) with the fitting of the model that includes all the considered predictors with their estimated non-null coefficients so as to verify if the increase in likelihood is significant; in this case, at least one of the p coefficients is to be expected as different from zero …”

“…Moreover, we decided to test a very simple approach to automatically define these diagnostic areas (Costanzo et al 2012a(Costanzo et al , 2012bRotigliano et al 2011): we first generated a landslide identification point (LIP) for each landslide by piking from the 2-m DEM the highest cells along the boundary of the polygons delimiting the landslide area, so that LIPs are positioned along the central sectors of the crown areas (Fig. 2b); we then identify the diagnostic areas in a buffer area of 8 m around the LIPs.…”