# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 5 Discussion
In contrast, the RF model's ensemble-based structure ensures greater robustness and generalization by reducing variance through the aggregation of numerous decision trees trained on bootstrapped datasets. This characteristic makes RF particularly effective in modeling complex nonlinear relationships while avoiding overfitting, even when handling high-dimensional geospatial data [17]. However, this strength in generalization may limit the model's ability to capture localized terrain variations and subtle feature interactions critical for high-resolution landslide delineation. As RF relies on majority voting from multiple shallow or randomly split trees, it may underrepresent small-scale spatial heterogeneity--especially in terrains with highly variable lithological or hydrological conditions [80]. The weaker performance of the NB model likely results from its assumption of conditional independence between features--an unrealistic premise in heterogeneous geospatial datasets. Similarly, CDT's conservative approach to splitting under uncertainty appears to underrepresent true spatial variability. These results highlight that model suitability is context-dependent and must account for not only accuracy metrics but also algorithmic behavior, data structure, and the trade-off between generalization and detail capture. Despite its simplicity and computational efficiency, the NB model's strong assumption of feature independence limits its ability to capture complex spatial interactions, which may contribute to its weaker performance in this study [55; 81]. Nevertheless, its robustness with small datasets and low sensitivity to missing values makes it a viable tool for rapid, preliminary susceptibility assessments. CDTs, while effective in managing uncertainty through imprecise probabilities, tend to underperform in highly variable terrain due to their conservative splitting mechanism. Their strength lies in interpretability and reduced risk of overfitting, yet this comes at the expense of spatial granularity in complex geohazard contexts [58]. In the present study, ASTER GDEM at 30 m resolution was utilized for deriving topographic factors due to its consistent spatial coverage, compatibility with other datasets, and proven effectiveness in regional-scale landslide studies. While this resolution was sufficient for identifying susceptibility patterns at a broader scale, we acknowledge that finer-resolution datasets, such as LiDAR-derived DEMs, could provide more detailed terrain representation and enhance the precision of susceptibility zoning, particularly in localized or highly variable terrain. Recent studies [82; 83] have demonstrated that DEM resolution significantly impacts the accuracy of slope-related hazard modeling. Similarly, [PERSON] et al. [84] emphasized the importance of high-resolution terrain data for extracting conditioning factors and validating results. Our methodology complements existing research by integrating a probabilistic framework with machine learning models, and we align with internationally endorsed protocols for landslide risk analysis [85]. However, future studies should explore the integration of high-resolution DSMs generated from UAV or LiDAR sources to improve terrain detail, particularly in micro-topographic features critical to slope instability. Additionally, coupling object-oriented image analysis techniques [86] with spectral and morphometric data could further refine landslide inventory generation and enhance model training. To advance the field, future work should also include time-series analysis of dynamic conditioning factors such as rainfall, vegetation changes (NDVI), and anthropogenic activity to capture temporal variability in susceptibility. Expanding the inventory with post-disaster UAV surveys and implementing more robust cross-validation strategies across multiple regions could further validate the generalizability of the proposed models.