## 1 Introduction
Random Forest (RF) enhances model stability and predictive accuracy by aggregating multiple decision trees. RF has proven highly reliable for LSP studies [20]. Despite the strong performance of machine learning models in LSP studies, data preprocessing and quality control account for approximately 80% of the modeling effort [19]. Data acquisition and processing accuracy directly affect model performance, emphasizing the importance of high-quality input data [21]. Most machine learning models used in LSP studies are classification algorithms, which require both positive and negative samples for training. Positive samples represent areas where landslides have occurred, while negative samples represent non-landslide-prone areas. In LSP studies, the acquisition of landslides (positive samples) typically relies on various data sources, including historical landslide records, remote sensing image interpretation, and field investigations. The quality of these data differs significantly; sometimes, they can accurately reflect landslide occurrences, resulting in high-quality landslide inventories, but sometimes they do not. However, the selection of positive samples is relatively standardized and somewhat reliable. On the other hand, there is no unified standard for selecting non-landslide samples (negative samples) [22; 23]. Current methods include random sampling in landslide-free areas, low-slope sampling based on topographic features, or the selection of negative samples outside the buffer zones of landslide boundaries. Generally, these methods often rely on subjective judgments or specific geographical factors and cannot entirely avoid the risk of mistakenly selecting potential landslide areas as negative samples. One of the challengesremaining is ensuring that the input data sufficiently represent the geographical characteristics of both landslide and non-landslide areas [19]. This uncertainty in sample selection may affect the accuracy of the final output. As a result, determining how to scientifically and reasonably select non-landslide samples has become a key issue in current research. Traditional studies often adopted a 1:1 ratio of landslide to non-landslide samples. However, this balanced sampling strategy does not fully reflect actual field conditions [14], leading to poor model performance in real cases. The sampling ratio between landslide and non-landslide samples is critical to prediction accuracy during training and testing [24]. To address this issue, some researchers proposed various strategies for dealing with imbalanced data in recent years, including oversampling and adaptive sampling methods. Although these methods somewhat alleviate the data imbalance problem, they still face the risks of information loss or overfitting. Therefore, optimizing the ratio of positive to negative samples, combined with a reasonable method for selecting non-landslide samples, remains a challenge in improving model performance. This study proposes three strategies for generating non-landslide samples to improve the appropriateness of sample selection under various topographic conditions. After comparing these strategies, a more reasonable sample selection approach is identified. This method offers a systematic way to select non-landslide samples by considering each study area's unique environmental and geological characteristics.