Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey

By Mehmet Lütfi Süzen, Vedat Doyuran. 2004

400 citations recorded by the source corpus.

In the last decades, landslide hazard assessment has attracted many researchers' attention. A number of parameters are suggested to be responsible to quantitatively explain the mechanism of landslides; many of these parameters are very important and factual. However, some data types and models are site-specific and could not be applied to different locations. Furthermore, the data stored in continuous parameter maps are divided into a number of classes arbitrarily, depending on the vision of the expert. Basically, this division controls the result of bivariate analysis. Besides, the responsible portion of the parameter map controlling the mechanism is also weighted arbitrarily. Based on these two facts, the class boundaries put a prejudice on the produced susceptibility/hazard maps, which result in dependence on the knowledge of the user rather than being dependent on the data and the fact itself. The aim of this study is to refine the previously defined methods in a more data-dependent trend. To achieve this goal, two new concepts: seed cells and percentile maps are introduced. Seed cells are the zones that are considered to represent the best undisturbed morphological decision rules (conditions before landslide occurs) and would be achieved by adding a buffer zone to the crown and flank areas of the landslide. To quantitatively classify the input parameter maps, the data distributions of seed cells in the parameter maps are divided into a number of classes on the basis of their distribution's percentile break-points upon which the parameter maps are directly dependent on the seed cell distributions, hence to the data itself.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/633aeb4e5d31fae04d4b2b1497b9b81f529a0ce4

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Citations in the corpus

Landslide susceptibility evaluation and hazard zonation techniques – a review (2020, methodology): “…through multivariate statistical approach is based on the relationship of relative contribution of each of the causative factor to the total landslide susceptibility in the area (Nandi and Shakoor 2009; Kanungo et al. 2009; Ayalew and Yamagishi 2005; Süzen and Doyuran 2004; Dai et al. 2001).”
An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, methodology): “Twoing tð Þ ¼ PLPR 4 X i PðijtLÞ PðijtRÞj jð Þ2 ! ð7Þ 4.2 Bivariate methods In bivariate statistical analysis, each factor map is combined with the landslide distribution map, and weighting values based on landslide densities are calculated for each parameter class (Suzen and Doyuran 2004).”
A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping (2018, background): “On the other hand, Suzen and Doyuran (2004) proposed a strategy called seed cell in generating decision rules of landslide occurrence.”
Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey) (2008, methodology): “Several applications on bivariate statistical methods (Clerici et al. 2002, 2006; Remondo et al. 2003; Suzen and Doyuran 2004a; Gokceoglu et al. 2005), multivariate statistical methods (Carrara 1983; Chung et al. 1995; Suzen and Doyuran 2004b; Can et al. 2005; Guzzetti et al. 2006; Lee and Pradhan”
Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression (2014, background): “In addition to these parameters, soil thickness and meteorological conditions that can be considered as a triggering factor and pre-failure conditions of the recent landslides can be considered in landslide modeling studies (Santacana et al. 2003; Suzen and Doyuran 2004; Segoni et al. 2012).”
Preparation of a geotechnical microzonation model using Geographical Information Systems based on Multicriteria Decision Analysis (2006, background): “landsliding (Dai et al., 2001; Suzen and Doyuran, 2004a ).”