Landslide susceptibility mapping in Mawat area, Kurdistan Region, NE Iraq: a comparison of different statistical models

Abstract. During the last decades, expansion of settlements into areas prone to landslides in Iraq has increased the importance of accurate hazard assessment. Susceptibility mapping provides information about hazardous locations and thus helps to potentially prevent infrastructure damage due to mass wasting. The aim of this study is to evaluate and compare frequency ratio (FR), weight of evidence (WOE), logistic regression (LR) and probit regression (PR) approaches in combination with new geomorphological indices to determine the landslide susceptibility index (LSI). We tested these four methods in Mawat area, Kurdistan Region, NE Iraq, where landslides occur frequently. For this purpose, we evaluated 16 geomorphological, geological and environmental predicting factors mainly derived from the advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite. The available reference inventory includes 351 landslides representing a cumulative surface of 3.127 km². This reference inventory was mapped from QuickBird data by manual delineation and partly verified by field survey. The areas under curve (AUC) of the receiver operating characteristic (ROC), and relative landslide density (R index) show that all models perform similarly and that focus should be put on the careful selection of proxies. The results indicate that the lithology and the slope aspects play major roles for landslide occurrences. Furthermore, this paper demonstrates that using hypsometric integral as a prediction factor instead of slope curvature gives better results and increases the accuracy of the LSI.