## 4 Uncertainty Analysis
### Selecting Positive and Negative Samples
Compared to the random forest  
Figure 3: Landslide elements diagram [39]: (a) crown; (b) main scarp; (c) head; (d) minor scarp; (e) debris deposit; (f) toe. model using randomly selected non-landslide points, their susceptibility zoning results exhibited higher predictive accuracy. [PERSON] et al. [101] aimed to overcome the limitation of low accuracy in non-landslide samples. They utilized the information value model for zoning and selected non-landslide samples from areas with low susceptibility. These samples were then combined with landslide samples and fed into a neural network model for training. As a result, the model's accuracy improved by 5.1%. [PERSON] et al. [102] proposed an objective method based on the Mahalanobis distance (MD), chi-square distribution, and user-specified confidence levels to determine non-landslide points independent of the landslide-influencing factors. In comparison to the commonly used methods based on slope angle for selecting non-landslide points, this approach is more objective and exhibits better consistency. Especially in areas with incomplete landslide inventories, where the existing landslide points lack representativeness, the MD method can identify a safe sampling area for non-landslide points, enhancing the robustness and effectiveness of the evaluation results. The methods used in the relevant literature and the publication years of the papers referred to in the above discussion are shown in Table 4.  
The topic of whether the ratio of positive to negative samples affects prediction accuracy has also been studied by some researchers. Based on the comprehensive grid management mechanism used in Dengfeng City, [PERSON] et al. [103] conducted a landslide susceptibility assessment in the study area using a random forest model. They utilized landslide data obtained through field surveys and heterogeneous data acquired from aerospace technology. During the evaluation process, the authors set up two control groups. One group used all the evaluation factors in the model, with a landslide point to non-landslide point ratio of 1:1. The second group employed a random forest weighting analysis method to calculate the Gini coefficient, thereby determining the contribution rates of each factor. They removed evaluation factors with lower contribution rates and adjusted the landslide point to non-landslide point ratio to 1:2. Through the optimization of evaluation factors and the increase in the number of non-landslide points, the model's accuracy, precision, recall, and FI score improved from 0.77, 0.78, 0.94, and 0.85 to 0.84, 0.87, 0.93, and 0.90, respectively. The accuracy of landslide risk exceeded 0.8, indicating the effective application of the random forest model. Moreover, the optimization of evaluation factors and the increase in the number of non-landslide points significantly enhanced the accuracy and comprehensiveness of the model's predictions. The increase in non-landslide points allowed the model to better learn non-landslide characteristics, thus improving its representativeness and generalization ability. Finally, the authors emphasized the importance of enriching the landslide evaluation factor library, which is crucial for improving assessment frameworks and enhancing the accuracy and applicability of future disaster predictions.