## 5 Discussion  
Comparative Analysis of Application Effects and Novel Approach Advantages in Negative Sampling Methods  
Distinct negative sample selection methods significantly impact the quality of negative samples. To rigorously validate the effectiveness of the proposed sampling approach, this study generated spatial distribution maps of negative landslide samples using the geographic similarity-based sampling method (Figure 10a) and the FR model sampling method (Figure 10b) [9; 28].  
Figure 10a shows the credibility distribution map of the negative samples, which was created by integrating the kernel density of the evaluation factors within a search radius of 500 m. The map illustrates that regions with a higher density of landslides correspond to the lower credibility of negative samples, whereas areas with a lower density of landslides demonstrate higher credibility. Although this method successfully confines low-credibility areas to those adjacent to near landslides, it fails to map the environmental feature space defined by evaluation factors throughout the study area, inadequately reflecting the distribution characteristics of potential landslides. Figure 10b shows the landslide susceptibility map constructed using the FR model. Using the constructed map to select negative samples from low-susceptibility areas has several limitations. First, the FR values of the evaluation factors are not continuous, which leads to low continuity in the landslide susceptibility map and pronounced spatial patchiness, thereby complicating the selection of negative samples. Second, classifying landslide susceptibility involves subjectivity; this study categorizes the susceptibility of the study area into five levels with intervals of 0.2. Most areas are classified  
Figure 10: Spatial manifestations of two negative sample sampling strategies: (**a**) geographic environmental similarity sampling and (**b**) FR model sampling.  
as extremely low or low susceptibility, but they also contain many historical landslides, which results in the quality of negative samples not being guaranteed.  
Although methods such as geographic environmental similarity and FR model sampling primarily address the spatial delineation of \"where to select\" negative samples (i.e., defining nonlandslide areas), a quantitative evaluation standard for the \"reliability\" of selected samples is yet to be established for these models. This limitation impedes the systematic analysis of the effect of variations in negative sample quality on the performance of landslide susceptibility assessment models [4; 29]. In contrast, the proposed fuzzy membership-based credibility sampling method achieves a fundamental breakthrough: it pioneers an intuitive and quantitative expression of negative sample reliability through \"credibility\" metrics. By generating a spatially continuous credibility distribution map, the proposed approach not only reveals the quality gradients of negative samples across regions but also provides an objective basis for screening high-quality samples (i.e., high-credibility samples). The proposed method significantly enhances both the theoretical foundation and operational feasibility of producing high-precision landslide susceptibility maps.