## 3 Methods  
The workflow of this study is illustrated in Figure 2.  
(1) Landslide Dataset Construction: this study first established a precise historical landslide distribution layer by georeferencing and spatially positioning the collected historical landslide inventory data for the research area using the ArcGIS platform. Subsequently, 14 evaluation factors were selected: (1) elevation, (2) slope, (3) aspect, (4) surface cutting depth (SCD), (5) plan curvature (PLC), (6) profile curvature (PRC), (7) engineering geological group (EGG), (8) soil erosion intensity (SEI), (9) soil thickness (STK), (10) soil moisture content (SMC), (11) population density (PD), (12) road density (RD), (13) fault density (FD), and (14) drainage density (DD). All raw factor data underwent standardized preprocessing in ArcGIS, including unified spatial reference conversion, projection adjustment, resampling (to a consistent resolution), and research area clipping. Topographic factors (elevation, slope, aspect, SCD, PLC, and PRC) were derived using Digital Elevation Model (DEM) surface analysis tools, and density-based factors (RD, FD, DD, and PD) were generated using the kernel density estimation method. Finally, a structured landslide susceptibility assessment database was constructed by spatially integrating the historical landslide distribution layer with the preprocessed raster data of the 14 evaluation factors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Source** & **Data Type** & **Scale** \\ \hline Landslides & Chengdu Geological Survey Center & Shop & \\ DEM & Global digital elevation model(GDEM) & Tiff & 90 m \\ Geological information & National Geological Data Center & Shop & 1:200,000 \\ Roads & Digital Earth Science Platform & Shop & 1:100,000 \\ Rivers & Resource and Environmental Science Data Platform & Shop & 1:100,000 \\ Faults & National Earthquake Data Center & Shop & 1:100,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multisource data and parameters for landslide assessment in the Liangshan Prefecture. Figure 2: Workflow of the sampling method based on fuzzy membership credibility for landslide susceptibility assessment. (2) Quantification of Negative Sample Credibility and Construction of Negative Sample Sets: first, evaluation factors were screened based on their coefficient of variation, nonlinear correlation with landslides, and collinearity diagnosis, ultimately selecting elevation, SEI, and SMC. Using the fitting tools in Origin software, the functional relationships between the selected evaluation factors and landslide frequency (the relative number of landslides occurring within specific factor classes) were fitted to construct fuzzy membership functions. Weighted by the proportional contribution of the correlation of each factor (weight = factor correlation/sum of all factor correlations), a spatially continuous negative sample credibility distribution map was generated through comprehensive weighted calculations in ArcGIS version 10.8.2 using the Raster Calculator. Based on predefined credibility thresholds, five negative sample sets were constructed: Set A (credibility: 0.5-1.0), Set B (credibility: 0.6-1.0), Set C (credibility: 0.7-1.0), Set D (credibility: 0.8-1.0), and Set E (credibility: 0.9-1.0). (3) Construction of the Frequency Ratio-Random Forest Coupled Model and Susceptibility Mapping: Integrated training samples were constructed by combining positive samples and five negative sample sets of varying credibility (Sets A-E). Based on the frequency ratio-random forest (FR-RF)-coupled methodology, Models A-E were trained separately. The impact of negative sample credibility thresholds on model performance was rigorously evaluated using the F1-score, AUC, and landslide ratio metrics to identify the optimal credibility threshold for landslide susceptibility assessment in the Liangshan Prefecture.