## 4 Uncertainty Analysis
### Selecting Positive and Negative Samples  
In machine learning, the selection of positive samples (landslide points) and negative samples (non-landslide points) has a significant impact on the evaluation results. Positive sample compilation is typically achieved through synthetic aperture radar (SAR) interferometry [90], Google Earth imagery interpretation [91], and field surveys. Google Earth imagery is commonly used to collect landslide data for assessing landslide risk [92]. Additional information can also be found on government websites. To investigate the influence of different landslide boundaries on landslide susceptibility assessment, researchers [93] in Ruijin City constructed three models based on points, circles, and polygons for landslides. The results showed that modeling landslide boundaries more accurately led to better precision, less uncertainty, and susceptibility indices that closely matched the real-world probability distribution observed in the field. [PERSON] [39] compared evaluation models that used area units versus point units. The results showed that the model based on area units had a higher evaluation accuracy. Furthermore, after removing the landslide front edge, the accuracy of the models further improved. When selecting point units, points in the middle of the slope were found to best represent landslides compared to those behind or in front of the slope, and their evaluation accuracy was higher than the other two (Figure 3). The optimized selection of negative samples can overcome any overfitting phenomenon in the model. The rational selection of negative samples has a significant impact on improving the accuracy of susceptibility evaluation [14; 94]. The random selection of samples from landslide mapping by manual interpretation may result in negative samples being chosen as landslide points, due to missed or misinterpreted landslides during the interpretation process. [PERSON] et al. [95] studied the impact of different training datasets on landslide susceptibility assessment. They prepared five sets of randomly generated datasets, and the results showed variations in landslide susceptibility assessments based on these different datasets. This indicates that the selection of randomly generated data significantly affects the assessment outcomes. Furthermore, it demonstrates that landslide susceptibility assessments based on randomly generated non-landslide points are not accurate. Previous studies have attempted various approaches for selecting negative samples. [PERSON] et al. [96] chose areas with zero slope as negative samples, but the evaluation results showed that the contribution of the slope factor was much greater than other factors. In contrast, [PERSON] et al. [97] used high-resolution Google Earth imagery to interpret low-slope areas such as river channels and valleys in the study area and selected negative samples from them. Although this method ensured the stability of the negative samples, it either exaggerated or underestimated the contribution of the slope factor to the susceptibility model [98]. [PERSON] et al. [99] used the frequency ratio method to select negative samples, and the comparative results showed that the improved model achieved an increase in evaluation accuracy. [PERSON] et al. [14] employed the frequency ratio method (FR) to select negative samples from areas with extremely low and low susceptibility. They coupled this strategy with random forest (RF) and gradient-boosting decision tree (GBDT) models (FR-GDBT and FR-RF). The results demonstrated that utilizing the frequency ratio method to select negative samples from low-susceptibility areas can significantly enhance the accuracy of the predictive model. [PERSON] et al. [100] selected non-landslide points from areas with relatively lower and low susceptibility formed by the information value model. They combined this approach with a random forest model.