GIS-based spatial prediction of landslide susceptibility using logistic regression model

Abstract

In the present study, logistic regression analysis has been used to create a landslide hazard map for Sajarood basin, Northern Iran. At first, an inventory map of 95 landslides was used to produce a dependent variable, a value of 0 for absence and 1 for presence of landslides. The effect of causative parameters on landslide occurrence was assessed by the corresponding coefficient that appears in the logistic regression function. The interpretation of the coefficients shows that the road network plays the major role in determining landslide occurrence. Elevation, slope curvature, rainfall and distance to fault were excluded from the final analysis, because these variables do not significantly add to the predictive power of the logistic regression. After running the final probability function into Arc/view 3.2 software, a landslide susceptibility map has been produced. The accuracy assessment shows an overall accuracy of the landslide susceptibility map to be 85.3%. An area of 53.01% is found to be located in a very low, 18.33% in low, 20.96% in moderate and 7.7% in high-risk regions. The proposed susceptibility map was tested using -2LL, Cox and Snell R 2 , Nagelkerk R 2 and Roc procedure, and it is found to be very reliable.

GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece

“…Several researchers have applied these methods, which are either bivariate [26,27] or multivariate analyses [28,29]. In bivariate analysis, each individual landslide-influencing factor is combined with a landslide inventory map, and weight values based on landslide densities are calculated for its corresponding classes.…”

Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks

“…Unfortunately, the literature shows that only a few models have been proposed. These models include some statistical models, such as the information value model (Li et al 2019), hierarchical clustering (Sheth et al 2001), the conditional probability model and the logistical regression model (Mousavi et al 2011;Papadopoulou-Vrynioti et al 2013;Sun et al 2017). In recent years, in addition to these statistical models, a few machine learning models have also been applied to carry out CSA, including the fuzzy mathematic method (Srivastava et al 2010;He et al 2013) and back-propagation neural network (Yilmaz et al 2013;Chen et al 2017;Li et al 2019).…”

Relative tectonics and debris flow hazards in the Beijing mountain area from DEM-derived geomorphic indices and drainage analysis

“…Assessment of debris flow disasters often focuses on the regional/ macro scale, and most researchers choose large-scale drainage basins as study objects for which the practical significance of the assessment results should be addressed (Lee, 2005;Chen and Wang, 2007;Pradhan, 2010;Mousavi et al, 2011). In this study, we performed an analysis on a relatively large region that extends over the Beijing mountain area in China.…”

“…Various approaches for hazard analysis of debris flows and landslides have been developed by many researchers. These approaches include inventory analysis (Hewitt, 1998;Guzzetti, 2000), logistic regression (Lee, 2005;Chen and Wang, 2007;Mathew et al, 2007;Pradhan, 2010;Mousavi et al, 2011), multivariate statistical analysis models based on GIS and remote sensing techniques (Liu et al, 2004;Fourniadis et al, 2007;Lee and Choi, 2004;Manzo et al, 2012), and real-time debris flow hazard management for early warning (Jakob et al, 2012).…”