###### Abstract  
This study investigated the influence of non-landslide sampling strategies on landslide susceptibility assessment (LSA) performance and explored approaches to minimizing uncertainty in model selection. Five non-landslide sampling strategies were evaluated using the random forest (RF) model to generate landslide susceptibility maps (LSMs) for each scenario. To assess the impact of these strategies, this study employed a receiver operating characteristic (ROC) curve, a confusion matrix, and various statistical indicators. Additionally, the mean susceptibility indices derived from the gradient boosting decision tree (GBDT), support vector machine (SVM), and RF models were analyzed to evaluate their effectiveness in reducing the uncertainty during model selection. The GBDT, SVM, and RF were selected for their ability to handle complex, nonlinear relationships in the data, superior generalization capability, effective mitigation of overfitting risks, high predictive performance, and robustness. The findings revealed that selecting non-landslide samples from slope units without landslides enhances accuracy and averaging across models mitigated the uncertainty associated with landslide susceptibility models. Furthermore, this study demonstrated that the non-landslide sample selection method significantly improved prediction accuracy, particularly when samples were drawn from very-low-susceptibility zones identified by pre-classified machine learning models. These results highlight the importance of refining sample selection strategies and integrating multiple machine learning models to improve the reliability and accuracy of landslide susceptibility assessments. This approach provides valuable insights for future research and practical applications in risk mitigation and disaster management by offering a more precise depiction of low-susceptibility areas, thereby reducing the occurrence of false positives in landslide prediction. landslide susceptibility; machine learning; modeling uncertainty; remote sensing 2025152132  
18 January 202513 February 202513 February 20  
enhanced landslide spatial prediction [5]. Currently, landslide spatial prediction relies predominantly on GIS platforms to perform susceptibility assessments through multi-factor correlation analysis. Various models have been developed, including physical [6], traditional statistical [7], and machine learning models [2; 8; 9]. Among these, machine learning models have become a research focus because of their ability to overcome the limitations of traditional statistical methods, such as the need for predefined assumptions. Unlike traditional methods, machine learning models do not require input variables to follow a normal distribution and effectively capture nonlinear relationships between environmental factors and landslide susceptibility. These models are particularly adept at fitting highly nonlinear relationships. Commonly employed machine learning models include logistic regression, k-nearest neighbor, support vector machines, extremely randomized trees, Bayesian networks, artificial neural networks, gradient boosting decision trees, and random forests. For example, [PERSON] et al. [10] applied a logistic regression model to assess landslide susceptibility in the Wuling Mountains and demonstrated its strong accuracy in predicting landslide-prone areas. [PERSON] et al. [11] employed the k-nearest neighbor algorithm to evaluate landslide susceptibility in Southwestern China, showing that the model effectively handled landslide data in complex topographic conditions. [PERSON] et al. [12] combined support vector machines (SVMs) with geological and topographic factors, revealing that the SVM approach significantly improved prediction accuracy in complex geological environments while efficiently processing high-dimensional data. [PERSON] et al. [13] applied the extremely randomized trees model for landslide susceptibility assessment in the Qinling Mountains, highlighting its enhanced stability and accuracy under intricate geological conditions.