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.