Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran

Abstract

A remote sensing and geographic information system-based study has been carried out to map areas susceptible to landslides using three statistical models, frequency ratio (FR), logistic regression (LR), and fuzzy logic at the central Zab basin in the mountainsides in the southwest West Azerbaijan province in Iran. Ten factors such as slope, aspect, elevation, lithology, normalized difference vegetation index (NDVI), land cover, precipitation, distance to fault, distance to drainage, and distance to road were considered. Landsat ETM+ images were used for NDVI and land cover maps. A landslide inventory map of the study area was identified by a SPOT 5 satellite after which fuzzy algebraic operators were applied to the fuzzy membership values of landslide susceptibility mapping. In addition, FR and LR models were applied to determine the landslide susceptibility. The three models are validated using the receiver operating characteristic and the area under which curve values were calculated. The validation results showed that the LR model (accuracy is 96 %) has better prediction than fuzzy logic (accuracy is 95 %) and FR (accuracy is 94 %) models. Also, among the fuzzy operators, the gamma operator (λ = 0.975) showed the best accuracy (94.64 %) while the fuzzy OR operator when applied showed the worst accuracy (85.11 %).