Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method

Abstract

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method Abstract A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.

Landslide susceptibility mapping on a global scale using the method of logistic regression

“…The quantity of nonlandslide points should be carefully considered. Most studies use an equal number of landslide points and nonlandslide points (Dai and Lee, 2002;Kawabata and Bandibas, 2009;Chau and Chan, 2005;Costanzo et al, 2014;Regmi et al, 2014;Mathew et al, 2009). However, a few authors prefer an unequal number Felicisimo et al, 2013).…”

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

“…In spite of the striking criticism which arises when selecting only a very limited subset of the mapped areas, a number of papers, which exploit logistic regression methods to produce grid cell susceptibility models, optimizes very sophisticated statistic procedures but disregards the real spatial representativeness of the fitted models; in these studies, models are trained solely on very limited part of the mapped basins, which typically stretch for hundreds of square kilometers, without verifying if changes in the random extraction of negative cases result in modifying the selected factors or their regression coefficients (Akgün 2012;Chauan et al 2010;Erener and Düzgün 2010;Mathew et al 2009;Nefeslioglu et al 2008;Ohlmacher and Davis 2003;Süzen and Doyuran 2004). At the same time, some other papers in literature deal with the estimation of robustness in terms of stability of the statistical procedure, disregarding the problem of the geologic representativeness of the subset on which regression is applied (e.g., Carrara et al 2008;Vorpahl et al 2012), using totally boot strapping-based procedures.…”

“…) 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 (Hosmer and Lemeshow 2000). By exponentiating the β's, odds ratios (OR) for the independent variables are derived: these are measures of association between the independent variables and the outcome of the dependent, and directly express how much more likely (or unlikely) it is for the outcome to be positive (unstable cell) for unit changing of the considered independent variable.…”

“…It must be mentioned that in a number of papers diagnostic areas simply correspond to the whole landslide polygons, disregarding the morphodynamic heterogeneity of these sectors with respect to past phenomena (Chauan et al 2010;Das et al 2011;Felicísimo et al 2012;Mathew et al 2009;Rossi et al 2010). Some researches deal differently with this topic by adopting diagnostic areas that are defined on an analytical basis (Carrara et al 2008;Nefeslioglu et al 2008;Rotigliano et al 2011;Van den Eeckhaut et al 2009).…”

“…Among the statistical techniques and also in comparative studies (Akgün 2012;Felicísimo et al 2012;Guzzetti et al 2005;Mathew et al 2009;Rossi et al 2010;Vorpahl et al 2012), logistic regression (Hosmer and Lemeshow 2000) has proved to be one of the most suitable and performing methods for assessment of landslide susceptibility on basin scale. The wide use of this multivariate technique for landslide susceptibility modeling is mainly due to its capacity to work on any type of independent variable (either ratio, interval, and ordinal or nominal scale), regardless of the deviance of the considered predictors and residuals from a normal distribution.…”