## 4 Discussion  
These are no universal guidelines for choosing landslide influencing factors, the number and range of the classes of these factors [51]. Therefore, based on the geomorphological and geological characteristics of the study area, data availability and other landslide susceptibility studies, fifteen influencing factors were taken into account for modelling the process in this work. Meanwhile, the SWARA technique was used to assess the correlation between landslides and influencing factors. Accordingly, the SWARA weights revealed that most landslides occurred with a slope above 47.6\({}^{\circ}\). In this case, the shear stress is dominated on the shear strength which results in landslide incidence using the gravity force. Also, the south-facing slope is more susceptible to landslide occurrence in comparison to the other directions. Since most of the rainfall in the study area occurred in this aspect, wet-and-dry cycles lead to decreasing the shear strength of soil and creating the landslide. This result is in concordance with the work in [51], which states that the rainfall is a crucial factor for landslide occurrence on south-facing slopes. When the altitude is lower than 432 m or higher than 1032 m, the frequency of landslide occurrence is very low. Although most of the landslides (148 landslides) occurred in the class of 432-582 m which covered an area of about 22.87%, the highest SWARA weight was obtained between 882 and 1032 m. However, at the middle altitude, 14 landslides occurred only have an area of 1.46% of the study area. The plan and profile curvature are less than -0.001, which represent that concave (negative) from of slope is the most susceptible to landslide occurrence due to having more concentration of water and erosion rather than convex (positive value) and flat (zero value). The SPI can indicate the erosion power of the stream, and the SWARA weights are larger as the SPI is increased. However, few landslides (only 3 landslides) occurred in the last class of this factor (800-1000). The STI classes of 30-40 and 50-60 obtained the highest SWARA weight, which showed the significant influence on the landslide occurrence. The SWARA weight of TWI increases when larger areas are involved in runoff generation. Since 200 landslide locations (63.49%) occurred in TWI between 3 and 5 (36.93% of the study area), this class of TWI was more effective in landslide occurrence. The most significant class of lithology was obtained for C unit (Kimberley rock) with the  
Figure 16: ROC curves for all methods using (**a**) the training dataset and (**b**) the verification dataset. SWARA weight of 0.36. It was noted that only one landslide event occurred in this class of lithology, which covered the smallest section of the study area (less than 1%). The highest SWARA weight (0.21) was acquired for distance to fault less than 500 m, which indicated that about 19% of the landslides occurred due to fault activities in 13.63% of the study area. Results of correlation between distance to river and landslide occurrence also depicted that the class of 600-750 m distance from the river was more effective on landslide incidence in the study area. The land use has indirect impact on landslide occurrence. Basically, forest class of land use was specified as the most susceptible class rather than the other classes due to having 243 landslides (77.14%) on 48.52% of the study area. The results also concluded that NDVI between 0.35 and 0.45, ACu type of soil, and rainfall between 1500 and 1600 mm were recognized as the most susceptible classes to landslide occurrence rather than other classes of influencing factors. In other words, these classes were more effective and had a significant role on landslide modelling process and preparing susceptibility maps. Recently, hybrid machine learning model had been widely applied for landslide susceptibility modelling, for example, [PERSON] et al.