A regional scale basin susceptible to landslide located in Qaemshahr area in northern Iran was chosen for comparing the reliability of weight of evidence (WofE), fuzzy logic, and frequency ratio (FR) methods for landslide susceptibility mapping. The locations of 157 landslides were identified using Google Earth® or extracted from archived data, from which, 22 rockslides were eliminated from the data-set due to their different conditions. The 135 remaining landslides were randomly divided into two groups of modelling (70%) and validation (30%) data-sets. Elevation, slope degree, slope aspect, lithology, land use/cover, normalized difference vegetation index, rainfall, distance to drainage network, roads, and faults were considered as landslide causative factors. The landslide susceptibility maps were prepared using the three mentioned methods. The validation process was measured by the success and prediction rates calculated by area under receiver operating characteristic curve. The ‘OR’, ‘AND’, ‘SUM’, and ‘PRODUCT’ operators of the fuzzy logic method were unacceptable because these operators classify the target area into either very high or very low susceptible zones that are inconsistent with the physical conditions of the study area. The results of fuzzy ‘GAMMA’ operators were relatively reliable while, FR and WofE methods showed results that are more reliable.

This study investigates how effective the receiver operating characteristic (ROC) curve is for comparing the reliability of landslide susceptibility maps (LSMs). In this regard, we selected a basin prone to landslides in northern Iran and employed frequency ratio and weight-of-evidence methods for modelling. Modelling for each method was done by considering three fractions of training/test landslides (50/50, 60/40, and 70/30) and, in addition, three random seeds for each of these fractions, leading to produce 18 LSMs. Validation rates of LSMs were obtained through calculation of the area under the ROC curve. Moreover, Cohen's kappa index was calculated to reveal the magnitude of the agreement between the maps. Results showed that all LSMs, despite having equal validation rates, were considerably different in terms of spatial prediction pattern. It is concluded that, although as a prevalent validation tool, ROC curve is only an indicator of the general reliability of geographical prediction maps and cannot reveal the uncertainty of spatial prediction patterns. Therefore, to reduce the conflicts between the maps and create a single reliable map, all LSMs were merged using the overlaying statistical functions that can extract the best possible zone pattern from all the overlapped patterns.

The aim of this study was to apply data mining algorithms to produce a landslide susceptibility map of the national-scale catchment called Bandar Torkaman in northern Iran. As it was impossible to directly use the advanced data mining methods due to the volume of data at this scale, an intermediate approach, called normalized frequency-ratio unique condition units (NFUC), was devised to reduce the data volume. With the aid of this technique, different data mining algorithms such as fuzzy gamma (FG), binary logistic regression (BLR), backpropagation artificial neural network (BPANN), support vector machine (SVM), and C5 decision tree (C5DT) were employed. The success and prediction rates of the models, which were calculated by receiver operating characteristic curve, were 0.859 and 0.842 for FG, 0.887 and 0.855 for BLR, 0.893 and 0.856 for C5DT, 0.891 and 0.875 for SVM, and 0.896 and 0.872 for BPANN that showed the highest validation rates as compared with the other methods. The proposed approach of NFUC proved highly efficient in data volume reduction, and therefore the application of computationally demanding algorithms for large areas with voluminous data was feasible.