## 5 Discussion  
This section focuses on three key aspects: (1) the primary findings derived from the experimental results; (2) the remaining uncertainties present in the modeling process; and (3) the limitations of this study and suggestions for future research priorities. Figure 12: Proportions of areas and landslides across different susceptibility zones under various non-landslide sample sampling strategies: (**a**) area proportions; (**b**) landslide proportions. Figure 13: ROC curves and AUC values plotted using five different datasets. The uncertainties that this study seeks to address can be categorized into two main aspects: (1) the effect of non-landslide sample selection on susceptibility modeling; and (2) the impact of machine learning model selection on prediction outcomes. Specifically, when an 800 m buffer was applied around landslide points, the susceptibility zoning generated using non-landslide samples from outside the buffer indicated a significantly lower predictive accuracy than the samples drawn from slope units without landslides. Geologically, this discrepancy can be identified because non-landslide samples from outside the buffer zone may include environments and geological conditions similar to those that favor landslide development [18]. In contrast, selecting non-landslide samples from slope units without landslides effectively eliminated this issue. Statistical analyses, confusion matrix metrics, and AUC values confirmed this finding, demonstrating substantial improvements when the non-landslide samples were selected from slope units devoid of landslides. This study further confirmed that selecting non-landslide samples from the very-low-susceptibility zone identified by machine learning models could enhance susceptibility modeling performance, yielding higher AUC values than selecting non-landslide samples outside the buffer zone. However, the predictive performance was closely tied to the accuracy of the pre-classified machine learning model. Generally, a higher accuracy in the pre-classified model could contribute to better classification performance using the RF model. Conversely, lower accuracy in pre-classified models, such as the LR model in this study, may lead to significant discrepancies between the RF model predictions and actual performance. Additionally, integrating multiple machine learning models has been proven to mitigate the impact of model selection on landslide susceptibility modeling; however, this approach requires inherently high prediction accuracy for each model. In this study, uncertainties persisted, primarily in two aspects: (1) the selection of landslide samples; and (2) the selection of influencing factors. First, the sampling method for the landslide samples in this study was based on the length and width of the landslides recorded in the landslide catalog data to calculate their area. Then, each landslide was approximately regarded as a circle, with the radius derived from the area. A buffer was created based on the radius, and a portion of the slope unit containing the landslide was extracted as a landslide sample. This method inevitably deviates from the true extent of the landslide area, potentially including non-landslide areas or excluding parts of the actual landslide in the sample. Second, the uncertainty introduced by the selection of influencing factors is closely related to the model's predictive performance. Although numerous influencing factors have been incorporated into landslide susceptibility modeling, no universally optimal combination of factors exists [39]. Some studies have demonstrated that modifying the selection of factors, by either adding or removing certain factors, can enhance predictive ability [40; 41]. In this study, 14 influencing factors were selected based on previous research experience; however, the uncertainty in factor selection was not investigated because it was not the primary focus of our research.