###### Abstract
[PERSON] et al. [14] implemented a Bayesian network model in the Sichuan Basin, demonstrating its effectiveness in handling various uncertainties related to landslide occurrence. [PERSON] et al. [15] applied artificial neural networks (ANNs) for landslide susceptibility modeling in the Loess Plateau, showing that ANNs effectively captured nonlinear relationships in landslide-prone areas, improving prediction accuracy. [PERSON] et al. [16] employed gradient boosting decision trees (GBDTs) in the Three Gorges Reservoir area, finding that GBDTs enhanced prediction accuracy through the integration of multiple weak classifiers while effectively addressing variable geological conditions. [PERSON] et al. [17] applied random forest models to assess landslide susceptibility in the Tibetan Plateau, demonstrating their capability to handle high-dimensional data, identify key factors influencing landslides and provide efficient predictive results. Despite these advancements, significant uncertainties persist in the application of machine learning models for the spatial prediction of landslides, particularly concerning the selection of LSA models and data sampling. The suitability of LSA models varies across research areas, leading to considerable variability in results among researchers using different models. This variability makes discrepancies in LSA outcomes evident and hinders identification of the model with the best predictive performance, thereby limiting the application and promotion of LSA findings. Consequently, improving the applicability and generalization of machine learning models in regional LSA remains a critical area for further research [18]. Non-landslide samples serve as target label data for landslide susceptibility modeling and play a crucial role in determining the applicability of susceptibility models. Thus, careful and rational selection of non-landslide samples is essential for enhancing the accuracy of LSA. The current literature identifies three primary methods for selecting non-landslide samples: (1) buffering outward from the edges of landslides to generate sample data [19; 20]; (2) randomly selecting samples from non-landslide areas [21; 22]; and (3) selecting samples from regions with gentle slopes that do not experience landslides [23; 24]. However, these methods involve varying degrees of subjectivity and are influenced by the characteristicsof specific factors, thereby introducing uncertainties into the selection of non-landslide samples. These challenges hinder the accurate prediction of landslide susceptibility, making this a critical and active area of research. Badong County, located at the heart of the Three Gorges Reservoir area, is highly susceptible to landslide-induced geological hazards. Among the regions within the Three Gorges Reservoir, Badong County has experienced a particularly high frequency of landslides. Since the impoundment of reservoirs, fluctuations in water levels and associated issues such as reservoir bank landslides have attracted significant attention. Notably, during periods of high water levels, landslide occurrence increases, posing substantial risks to local infrastructure, public safety, and the ecological environment. This study addressed the critical need for disaster prevention and mitigation by conducting a regional landslide susceptibility assessment. Effective strategies were explored to minimize uncertainties related to non-landslide sample selection and model choice, with the aim of producing more accurate and reliable landslide susceptibility zoning results. These findings will serve as a solid foundation and valuable reference for future disaster prevention, mitigation efforts, and regional landslide risk assessment. This study addressed the uncertainties in landslide susceptibility modeling by proposing the application of average susceptibility indices from multiple models as the final susceptibility index to reduce uncertainty in model selection.