GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis

Abstract

Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area. An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed, Kodaikkanal, India using binary logistic regression analysis. Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map, which is used to build the spatial model of landslide susceptibility. The model describes the relationship between the dependent variable (presence and absence of landslide) and the independent variables selected for study (predictor variables) by the best fitting function. A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis. An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable. Relief, slope, aspect, plan curvature, profile curvature, land use, soil, topographic wetness index, proximity to roads and proximity to lineaments are taken as independent variables. The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in the study area. The model shows that the most significant parameter contributing to landslides is slope. The other significant parameters are profile curvature, soil, road, wetness index and relief. The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

“…Fixing the sample size to create an equation in logistic regression analysis can be done in two ways, i.e., using all pixel landslide causal factors in study area and using equal number of dependent and independent variables to reduce bias in the sampling process (Ramani et al 2011). In this study, the logistic regression model is developed using equal proportion of landslide and nonlandslide pixels in ten iterations and using 50 % and all non-landslide data as comparison.…”

Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey)

“…In the literature, the logistic regression method has been used widely to produce landslide susceptibility maps (i.e., Carrara et al 2008;Nefeslioglu et al 2008;Bai et al 2010;Pradhan 2010;Chauhan et al 2010;Atkinson and Massari 2011;Ramani et al 2011;Yalcın et al 2011;Akgun 2012;Choi et al 2012;Ozdemir and Altural 2013;Wang et al 2013;Althuwaynee et al 2014;Shahabi et al 2014). The first step of the logistic regression analyses was preparation of the data matrix.…”

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

“…A positive LR coefficient indicates that the existence of the conditioning factor in the area increases the probability of the flood creation. The negative logistic coefficient values imply that the occurrence of flooding is negatively related to that specific factor (Chauhan et al 2010;Ramani et al 2011). …”