## 6 Discussion  
The susceptibility of an area to landslides is a function of different possible conditioning factors. As all of the factors might have no predictive capability, the most important must be objectively chosen to strengthen the performance and accuracy of the learning algorithms in the training phase. In this study, we used the IGR technique to identify factors with high predictive powers. The weight of each factor in the training phase was calculated using an entropy index. In this study, tested 18 conditioning factors with a raster resolution of 10 m and found that 10 factors were significant: Distance from the road, normalized difference vegetation index, land use, slope aspect, lithology, slope angle, precipitation, distance from faults, elevation, and topographic wetness index. Eight factors were removed from final modeling because they had AM values of 0: e distance from stream, slope-length, annual solar radiation, profile curvature, plan curvature, fault density, drainage density, and stream power index. Researchers have used three main methods to display classes on landslide susceptibility maps: the natural break, geometrical interval, and quantile methods. We statistically tested the three methods for producing maps using the three machine learning algorithms investigated in this study. The natural breaks classification method was selected for the SVM and ADTree models, and the quantile method for the BLR model. Most of the researchers in landslide susceptibility mapping confirmed the capability of the natural break method to classify the LSIs [105; 106; 107; 108; 109]. The quantile method among the classification methods is generally the most effective and commonly used method [104; 110; 111]. [PERSON] et al. [16] applied the natural break, geometrical interval, and quantile and based on their histogram of landslide probability values selected the natural break classification method for the random forest algorithm. However, the geometrical interval method for the three ensemble models of rotation forest-based random forest (RF-RAF), bagging based random forest (BA-RAF), and random subspace-based random forest (RS-RAF) to produce shallow landslide susceptibility maps. Distance to the road is the factor most closely related to landslides in the study area. All the susceptibility maps showed that most landslides are less than 100 m from the road through the study area. The road is located at high elevation in wet areas (high topographic wetness index), which are other significant landslide conditioning factors. In recent years, the road through the study area has been widened, and new bridges have been constructed, changing the landscape and initiating instability along the road. The road is also trafficked by trucks and other heavy vehicles. Therefore, they should be more considered during road widening and other engineering construction in future. Figure 7: ROC curve and AUC for the SVM, BLR, and ADTree models: (**a**) training and (**b**) validation datasets. The second and third most crucial landslide conditioning factors are the normalized difference vegetation index land use. Most landslides in the study area have happened in unvegetated or sparsely vegetated areas, including rangeland. Slope aspect is another critical factor as landslides tend to occur on slopes oriented toward the northwest and west because these aspects experience more precipitation and runoff than other factors. Precipitation in the study area is higher than the average for the country. By considering landslides susceptibility maps and its histograms with three models, it can be concluded that the susceptible areas of landslides belong to a very high susceptible class. After selecting appropriate landslide conditioning factors, we prepared landslide susceptibility maps using three machine learning models and the natural break, geometrical intervals, and quantile classification methods. Our finding concluded that natural break and quantile had most concordance and consistency with the reality of the study area.