# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 4 Results  
### Landslide Conditioning Factor Analysis  
As previously discussed, landslide susceptibility assessments typically involve analyzing a wide range of landslide-related factors. It is common practice to apply various selection methods to identify the most relevant and influential factors that best align with the specific characteristics of the study area, ensuring accurate and reliable model performance [66]. In order to analyze the spatial relationship between landslides and various conditioning factors in this study, we applied the CF method [67]. As illustrated in Table S1 in Supplementary File, the analysis revealed that the slope aspect has the highest correlation with landslide occurrence, with the south-west slopes achieving the highest CF value (0.625). Additionally, the statistical data confirmed that this category contains the highest number of recorded landslides. Regarding the slope steepness, among the five classified slope categories, the 8.82-19.19\({}^{\circ}\) and 19.19-30.82\({}^{\circ}\) ranges showed a positive correlation with landslide occurrence. Elevation is important when analyzing the spatial relationship between factors and landslides. Within our study area, which spans elevations from 422 m to 3648 m, landslides were positively correlated with two specific elevation ranges: 422-798 m and 2399-3648 m. In contrast, certain terrain indices showed no relationship with landslide activity. For instance, the last two classes of the STI had CF values of \(-1\), indicating no recorded landslides in these categories. Similarly, when the SPI was greater than 573.93, the CF value was \(-1\), confirming zero landslide occurrences. The TWI also showed an inverse relationship with the landslide probability, where increasing TWI values corresponded with a decreasing certainty of landslide occurrence, with the last two categories reaching CF = \(-1\). Regarding the curvature, the plan curvature range of \(-0.05\) to 0.05 showed a negative CF value of \(-0.213\), while profile curvatures in the range of \(-34.05\) to \(-0.05\) had a slight positive correlation (CF = 0.089), with half of the recorded landslides falling within this category. Concerning the distance to faults, for areas greater than 2000 m, the correlation between this classification and the occurrence of landslides decreased (CF value of \(-0.24\)). On the other hand, when examining the distance to roads and when the area in question was within the range 0-300 m, there was a higher certainty regarding the occurrence of landslides. When examining the distance to rivers, we found that the 400-600 m (\(-0.77\)) and 600-800 m (0.49) ranges showed the lowest and highest correlations, respectively, with the occurrence of landslides. When the NDVI value rose in the negative range, the certainty of the occurrence of landslides also increased. When the NDVI value was \(-0.01\)-\(0.35\) (\(-0.03\)), the correlation with the occurrence of landslides decreased. Regarding the certainty of rainfall and landslide occurrence, when the rainfall was in the range of 463.58-484.42 mm/year (0.45), it was highly correlated with landslides, while other ranges were not significantly correlated with the landslide occurrence. Among the six different land use categories, grassland (0.553) exhibited the highest correlation with the occurrence of landslides. Different soil types had different correlations with landslides. Except for Cumulic Anthrosol (ATC) and Calcaric Fluvisol (FLC), the other soil types showed negative certainties regarding the occurrence of landslides in this study. For lithology, the highest correlation was found in group 2 (0.37), with groups 1 (\(-\)0.21) and 5 (\(-\)0.27) achieving the lowest correlations with the occurrence of landslides.