Building models for automatic landslide-susceptibility analysis, mapping and validation in ArcGIS

Abstract

In this paper, ModelBuilder TM in ArcGIS (ESRI) has been applied to landslidesusceptibility analysis, mapping and validation. The models (scripts), available for direct downloading as an ArcGIS tool, allow landslide susceptibility to be computed in a given region, providing a landslide-susceptibility map, with the GIS matrix method, and ensuring a quality validation. The paper details the steps needed for the model-building process, enabling users to build their own models and to become more familiar with the tool. The susceptibility model leads the user first through a Digital Elevation Model (DEM), depicting the morphological and morphometric features of the study area, and then through a Digital Terrain Model (DTM), useful as a source of landslide-determinant factors, such as slope elevation, slope angle and slope aspect. In addition, another determinant factor is the lithological unit, independent of the DEM. Once the determinant landslide factors are reclassified and in a vectorial format, all the combinations between the classes of these factors are determined using the geoprocessing abilities of ArcGIS. The next step for the development of the landslide-susceptibility model consists of identifying the areas affected by a given surface of rupture (i.e. source area) in every combination of the determinantfactor classes. This step leads to the landslide matrix based on a previously georeferenced landslide database of the region, in which the slopes are distinguished into two simple classes: with or without landslides. In the last stage, to build a landslide-susceptibility model, the user computes the percentages of area affected by landslides in every combination of determinant factors. In the resulting landslide-susceptibility map a progressive zonation of areas or slopes increasingly prone to landslides is performed.

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 …”

Exporting a Google Earth™ aided earth-flow susceptibility model: a test in central Sicily

“…Clerici et al 2006Clerici et al , 2010Chacón et al 2006;Conoscenti et al 2008;Del Monte et al 2002;Irigaray et al 2007;Jiménez-Pelvárez et al 2009). According to conditional analysis, the landslide probability of each UCU was estimated by computing its landslide density, given as fraction of unstable (BLIP) cells to total cells.…”

Assessment of data availability for the development of landslide fatality curves

“…Pereira et al (2014) have found that most landslides occur during the wettest months, reflecting the importance of rainfall-triggering mechanisms. Landslides can cause serious damage to buildings and infrastructure, to the environment, and to human life (Devoli et al 2007;Jiménez-Perálvarez et al 2009;Lacasse et al 2010). Further, landslides in populated areas can create health consequences that exceed the local capacity to respond, increasing both mortality and morbidity from disasters (Keim 2008).…”

“…The largest proportion of landslide risk analyses or landslide susceptibility analyses has had a technical approach focusing on the first part of the function, the hazard probability (P), by performing and developing methods for mapping landslide risk, stability analysis, and mechanisms triggering landslides (e.g., Guzzetti et al 2005aGuzzetti et al , 2006Guzzetti et al , 2012Jiménez-Perálvarez et al 2009;Melchiorre and Tryggvason 2015;Poli and Sterlacchini 2007;Roslee and Jamaluddin 2012;Salas-Romero et al 2015;Van Den Eeckhaut et al 2012). Potential landslide consequences (C i ) have not attained the same scientific interest despite the obvious importance it has on the estimated risk product (R).…”