## 5 Discussion
Despite these two sources of uncertainty, our results demonstrate that the overall uncertainty in the modeling process is within an acceptable range. The AUC accuracy of all sampling strategies exceeded 0.721, and the ROC values for most strategies exceeded 0.8. This enables us to focus on analyzing the impact of sampling strategies and integrating the results of the different models, rather than solely focusing on improving model performance. Given the uncertainties mentioned above, the main limitations of the current method and findings can be summarized as follows. First, landslide samples generated by buffering outward from the landslide point may fail to accurately represent the actual landslide area. Using deciphered landslide surface vectors as samples would address this issue. However, this approach can be labor-intensive and challenging to implement. Second, this study focused only on the impact of non-landslide sample selection strategies on susceptibility modeling, while landslide sample selection also played a critical role in influencing the outcomes. For instance, [PERSON] et al. [20] explored three landslide sample selection strategies along the landslide boundary, within the boundary, and in the landslide extension area, highlighting their significant impact on modeling. Similarly, [PERSON] et al. [42] selected two widely used models, SVM and RF, to develop a landslide susceptibility assessment using landslide points, buffer zones, and polygonal surfaces. Furthermore, the models implemented in this study represented only a small subset of the available machine learning methods, limiting the ability to reduce the uncertainty associated with model selection. With limited research in this area, averaging the results from multiple models could offer little basis for meaningful comparisons with other approaches to address model selection uncertainty. Many researchers have explored suitable models for specific study areas by employing various sampling strategies, examining the effects of spatial resolution on susceptibility assessments, and analyzing the influence of different proportions of landslide and non-landslide samples. For example, [PERSON] et al. [43] investigated how different non-landslide sample selection methods and varying ratios of landslide to non-landslide to non-landslide samples affect landslide susceptibility modeling. [PERSON] et al. [44] compared the impact of different spatial resolutions (30, 40, 50, 60, 70, 80, and 90 m) on landslide susceptibility modeling using three models: frequency ratios, entropy indices, and weights of evidence. [PERSON] et al. [45] analyzed the effects of environmental factors at resolutions ranging from 30 to 600 m, with 30 m intervals, on landslide susceptibility assessments. Similarly, [PERSON] et al. [46] investigated the impact of varying ratios of landslide to non-landslide samples on landslide susceptibility modeling using a Bayesian optimization method. Hence, developing and testing alternative sampling techniques for both landslide and non-landslide cases as well as exploring innovative strategies for minimizing model selection uncertainty are essential. These advancements can provide valuable insights and significantly improve LSA. In conclusion, the improvement observed from selecting non-landslide samples from slope units without landslides and very-low-susceptibility zones identified by pre-classified machine learning models holds significant potential for enhancing the accuracy and reliability of landslide susceptibility modeling. This refinement is particularly valuable for risk assessment and disaster management because it provides a more precise representation of low-susceptibility areas, potentially reducing the occurrence of false positives in landslide predictions. Such refinements could ultimately support more informed decision-making in risk mitigation efforts. This approach may prove instrumental in developing more reliable susceptibility maps and promoting proactive disaster prevention strategies, especially in landslide-prone regions.