# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 5 Discussion  
This study demonstrated that the highest correlation with the occurrence of landslides among a number of factors is the classification of the southwest slope direction (0.62), and that the certainty of landslides occurring is high at distances from the road in the range of 0-300 (0.60), with this certainty decreasing as the distance increases. The probability of landslides in calcareous Calcaric Fluvisol (0.44) and grasslands (0.55) is much higher than in other soil types and land use types, respectively. The certainty of landslides was 0.45 when the rainfall was in the range of 463.58-484.42, with other precipitation ranges having lower levels of correlation with landslides. Of the five categories delineated by the slope, the range of 19.19-30.82 had the highest certainty (0.40) associated with the occurrence of a landslide event. The remaining influence factors, such as the STI, SPI, TWI, planar curvature, and profile curvature, were all related to landslides to varying degrees depending on the type or range of values. There were also a few cases in which the CF value was \(-1\) and no landslides occurred. These patterns suggest that both topographic orientation and proximity to anthropogenic features play crucial roles in slope instability within the study area. The strong association with southwest-facing slopes may reflect prevailing climatic or geomorphic processes, such as sun exposure, prevailing wind directions, and differential moisture retention [75]. However, it is important to note that slope aspect may also be indirectly correlated with other geo-environmental variables that influence landslide development, relationships that are often not fully captured in purely statistical analyses. Previous studies have emphasized the statistical coupling between slope aspect and other landslide- predisposing factors [76], while others [77,78] suggest that local geological, hydrological, and morphological conditions can further modulate or even reshape slope aspect over time. These insights underscore the complexity of interpreting aspect-related correlations and highlight the need for multidimensional approaches that integrate both surface patterns and underlying geomorphic controls. Concerning the influence of road network, road construction frequently involves slope cutting, blasting, and vegetation removal, which significantly alter local terrain stability. As noted by [PERSON] et al. [77], roadside sections are particularly prone to failure due to mechanical disturbance and concentrated runoff and that poorly designed drainage and slope modifications near roads can significantly amplify landslide susceptibility. To optimize models' performance in landslide susceptibility assessment, the MLP, CDT, and RF models were fine-tuned by selecting the most effective parameters. The MLP model was tested with learning rate values between 0.1 and 1, along with varying momentum factors, to identify the optimal configuration. The RF model was optimized by adjusting the number of decision trees (iterations) and random seed values, ensuring stability and accuracy in the LSI computation. Similarly, the CDT model was reconfigured by modifying iteration counts and random seed values to enhance the classification accuracy and minimize uncertainty. Through this parameter tuning process, the most suitable settings for each model were determined, improving their predictive reliability and overall performance. In the present study, four partitioning methods were applied to classify the LSI values that were generated by different models. The CDT model exhibited severe chunking effects across the entire study area, while the NB model displayed notable chunking patterns, primarily in the upper half of the image.