## 4 Discussions
### Performances of Susceptibility Modeling  
Figures 8 and 12a-e show that most of the predicted landslide zones follow a drainage network in the region and correspond to the high and very-high susceptibility classes along with this network. Aspect-based analysis conducted using SVM results showed that these high and very-high classes are mostly attributed to the slopes facing NE to WSW through E and S. These aspects are considered as warmer aspects in the region and can be correlated with climatic conditions. This distribution of the susceptible classes modeled using the SVM approach with reference to slope aspect can be seen in Figure 11b. Similar results of the landslide abundance along such aspects were also produced by [PERSON] et al. [72] and [PERSON] and [PERSON] [14]. The variations in climatic conditions along these aspects can be attributed to some other factors [73]. For example, these aspects mainly constitute relatively mid-level values of NDVI with sparse vegetation and experience a large and intense portion of direct rainfall, especially on the ESE-SW direction. The other slopes along the W to NE through the NW, normally known as cooler aspects, mainly constitute dense forest and snow at relatively higher altitudes. As shown in Figure 7b, the contribution of LCLU, NDVI, slope aspect, and elevation plays a vital role in the abundance of such a hazard; therefore, the prediction results of susceptibility and aspect-based analysis coincide mainly with the existence of these hazards. As the area is comprised of a diverse topography, with sudden changes in terrain, slopes, and aspect angles at some places (southern and northern zones), the contribution of other factors in the modeling cannot be neglected in this region. With this diverse topography, it can be highlighted that landslides in the area are mostly a cumulative effect of all the above mentioned factors. Figure 12: Recent landslide (black rectangle) near Kaghan: (**a**) RF, (**b**) SVM, (**c**) maxENT, (**d**) GBM, (**e**) LR, (**f**) GE image. The modeled results showed a very good agreement with validation data during the AUC analysis. In this study, the performance analysis of the modeled results showed that all five models were reliable in achieving good results in the area, with AUC values above 0.83. The better performances of RF, SVM, and GBM were observed in the scanning results of the models using indices-based evaluation measures (Figure 10a,b). [PERSON] et al. [65] concluded that if the difference between the accuracies of the model using training and validation data is higher, then there is a possibility of overfitting the models during the tuning and training process. In this study, we used validation data to acquire ACC values of 0.87, 0.87, 0.73, 0.86, and 0.68 for the RF, SVM, maxENT, GBM, and LR models, respectively, which corresponds to minimal differences between the accuracies obtained using the training data; therefore, the results obtained in this study showed fine-tuning, training, and modeling with higher reliability. From POA values of 2656 in RF, 2669 in SVM, 2623 in GBM, 1761 in maxent, and 1299 in LR (Figure 10b), we concluded the higher performance of the SVM model in this study followed by RF, GBM, maxENT, and LR. SVM models are considered to provide the best solution to a nonlinear process such as landslides, which makes this model best-suited to correctly predict the probability of landslide events by separating them with the help of a hyperplane [7]. [PERSON] [8] highlighted the ability of the SVM model to produce smooth prediction surfaces when compared with the tree-based models, with the presence of spatial artefacts in his results. Comparing results generated from LR and SVM, [PERSON] et al. [74] found that SVM produced a higher prediction accuracy. Similar findings of outperformance and the production of smoothed results in the SVM model have been reported by [PERSON] et al. [75] and [PERSON] [76] when performing a comparison with NN, DT, and LR models.