## 5 Discussions  
When the input features (i.e., the conditioning factors for landslides) in the area were considered, they were sufficient for modeling the susceptibility. The selection of the effective factors are highly dependent on the geoenvironmental settings; here the lithology, altitude and the TWI were determined as the most important ones. Although other conditioning factors can be found in the literature, such as normalized difference vegetation index (NDVI), rainfall, distance to roads, etc.; they were not separable (i.e., differentiating) in the study area and thus were not employed here. On the other hand, even though the topographic derivatives may have multicollinearity, the decision trees and thus XGBoost are known to be immune to this problem [55; 56]. [PERSON] [56] stated that parameter removal due to multicollinearity problem may yield to lower classification accuracy in decision trees. Additionally, a study by [16] evaluated similar landslide conditioning factors for multicollinearity and showed that the highest correlation value appeared between plan and profile curvatures, which was still below the critical value.  
Figure 17: Landslide susceptibility map produced in the study.  
Another important characteristic of the present dataset is the availability of a total of 27 lithological units (Table 2, Figure 3). Among those, ten of them involved no landslide inventory. Considering the potential incompleteness of landslide inventories, which is often the case in the literature, and the lithology being an important feature for susceptibility mapping (see Figures 15 and 16), such areas must be analyzed carefully by experts. Using feature importance metrics in LS studies is recommended for understanding the nature of the problem. In addition, although the land use land cover (LULC) information was not utilized in this study since the region is mostly rural and agricultural with some forest areas; it is recommended to update the LS maps when significant LULC changes occur.  
When the overall accuracy values obtained in this study (90.18%) were compared with the results of [PERSON] and [PERSON] [28], it was observed the accuracy values here were higher than the XGBoost (76.73%) and the deep neural network accuracy results (83.71%) of the mentioned study. Both the CatBoost (85.03%) and the XGBoost (83.36%) results provided by [PERSON] [29] are lower than the results obtained here. This situation can be explained by the use of different feature sets (conditioning factors), and the differences in DEM quality and the hyperparameter optimization. It should also be noted that in the present study, the size of the study area (2718.7 km\({}^{2}\)) was relatively large, which indicates that the results are promising for regional susceptibility assessment studies. On the other hand, when the LS prediction results from recent ML studies carried out in the East and Southeast Anatolia are compared; [PERSON] et al. [20] obtained AUC values of 0.90 and 0.92 using the RF method in an area of 425 km\({}^{2}\) using very high resolution aerial photogrammetric datasets [20]. [PERSON] et al. [15] evaluated logistic regression, ANN and RF methods in the close vicinity of a dam reservoir again by using very high resolution aerial photogrammetric datasets; and obtained AUC values of 0.76, 0.84 and 0.95, respectively. Although EU-DEM with lower spatial resolution was used here, the predicted AUC value was 0.96. The high accuracy values can be explained by the prediction power of the XGBoost method and the low noise level in the EU-DEM data.