Different ways of landslide geometry interpretation in a process of statistical landslide susceptibility and hazard assessment: Horná Súča (western Slovakia) case study

Abstract

The article draws a comparison between different ways of landslide geometry interpretation in the scope of the statistical landslide hazard and risk assessment processing. The landslides are included as a major input variable, which are compared with all of the input parametric factors. Based on the above comparison the input data are classified and the final map of landslide susceptibility is constructed. Methodology of multivariate conditional analysis has been used for the construction of final maps. Unique condition units was developed by combination of geological map (lithological units) and slope angle map. Lithological units were derived from geological map and subsequently reclassified into 22 classes. Slope angle map was calculated from digital elevation model (contour map at a scale 1:10,000) and reclassified into nine classes. As a case study, a wide area of Horná Súča (western Slovakia) strongly affected by landsliding (predominantly made of Flysch) has been chosen. Spatial data in the form of parametric maps, as well as final statistical data set were processed in GIS GRASS environment. Four different approaches are used for landslides interpretation: (1) area of landslide body including accumulation zone, (2) area of depletion zone, (3) lines of elongated main scarps, (4) lines of main scarp upper edge. For each approach, a zoning map of landslide susceptibility was compiled and these were compared with each other. Depending on the interpretation approach, the final susceptibility zones are markedly different (in tens of percent).

Explainable artificial intelligence in geoscience: a glimpse into the future of landslide susceptibility modeling

“…The very same period has also witnessed improvements in the choice of the geographic object to partition an area under study, with unique condition units (Bednarik & Pauditš, 2010;Titti et al, 2021), slope units (Carrara, 1983;Alvioli et al, 2016) and grid cells (Dhakal et al, 2000;P. M. Atkinson & Massari, 1998) becoming the most common choices, in ascending order (Reichenbach et al, 2018).…”

Assessing the natural hazard of gully erosion through a Geoecological Information System (GeIS): a case study from the Western Carpathians

“…Nathan, McMahon 1990;Ouarda et al 2001). In Slovakia, such methods have been used to assess landslide hazard (Pauditš, Vlčko, Jurko 2005;Bednárik, Pauditš 2009;Bednárik et al 2010). Furthermore, they have also been used to estimate the influence of local conditions on the overall impact of a windstorm within a forest located in the foothills of the High Tatra Mountains (Falťan et al 2009;Minár et al 2009).…”

Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system

“…In this regard, there have been studies on landslide susceptibility evaluation using GIS, and many of these studies have applied probabilistic models such as frequency ratio, weight of evidence etc. (Bednarik and Pauditš 2010;Lee et al 2004a;Lee and Min 2001;Lee and Pradhan 2006b;Mousavi et al 2009;Oh et al 2009;Ozdemir 2009;Pirasteh et al 2009;Pradhan and Youssef 2009;Regmi et al 2010;Yilmaz 2010). One of the statistical models available, the logistic regression model, has also been applied to landslide susceptibility mapping (Akgun and Bulut 2009;Bai et al 2010a, b;Chauhan et al 2010;Das et al 2010;Kincal et al 2009;Lee 2005Lee , 2007aNandi and Shakoor 2010;Oh et al 2009;Pradhan and Lee 2010a, b).…”