An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district

Landslide susceptibility maps are valuable sources for disaster mitigation works and future investments of local authorities in unstable hazard-prone areas. However, there are limitations and uncertainties inherent in landslide susceptibility assessment. For this purpose, many methods have been suggested and applied in the literature, which are generally categorized as bivariate and multivariate. Here, in this paper, the most popular and widely used multivariate [support vector regression (SVR), logistic regression (LR) and decision tree (DT)] and bivariate methods [frequency ratio (FR), weight of evidence (WOE) and statistical index (SI)] were compared with respect to their performances in landslide susceptibility modeling problem. Duzkoy district of Trabzon Province was selected due to its unique topographical and lithological characteristics, magnifying shallow landslide risk potential. Slope, lithology, land cover, aspect, normalized difference vegetation index, soil thickness, drainage density, topographical wetness index and elevation were employed as landslide occurrence factors. Accuracy measures based on confusion matrix (i.e., overall accuracy and Kappa coefficient) and receiver operating characteristic (ROC) curve were employed to compare the performances of the methods. Furthermore, McNemar’s test was employed to analyze the statistical significance of differences in method performances. The results indicated that multivariate approaches (i.e., SVR, LR and DT) outperformed the bivariate methods (i.e., FR, SI and WOE) by about 13 %. Within the multivariate approaches, SVR method performed the best with the highest accuracy, while FR method was the most effective and accurate bivariate method. Interpretation of AUC values and the McNemar’s statistical test results revealed that the SVR method was superior in modeling landslide susceptibility compared with the other multivariate and bivariate methods. Copyright Springer Science+Business Media Dordrecht 2015