## 3 Data and Methods
### Sample Data Preparation  
Current evaluation units for debris flow susceptibility assessment are primarily categorized into two paradigms: grid-based units, and watershed-based units [41]. Grid-based units rely on regular grid matrices, offering advantages such as high data compatibility and computational efficiency within GIS platforms [42; 43]. However, their discrete nature  
\begin{table}
\begin{tabular}{c c} \hline
**Data Type** & **Source of Information** & **Spatial Resolution** \\ \hline Debris Flow Hazard Spatial Dataset & Vector data \\ \hline Digital Elevation Model (DEM) & 12.5 m \\ \hline Road Network Vector Data & Vector data \\ \hline Land Use Raster Data & 30 m \\ \hline Geological Fault Line Vectors & The Resources and Environmental Sciences Data Center of the Chinese \\ \hline Annual Precipitation Raster Data & Academy of Sciences \\ \hline Soil Erosion Type and Intensity & 1 km \\ Classification Rasters & 250 m \\ \hline Population Spatial Distribution & 1 km \\ \hline \end{tabular}
\end{table}
Table 1: Multi-source spatial dataset summary.  
Figure 3: Evaluation process of debris flow susceptibility.  
limits their ability to systematically represent the overall watershed response mechanisms underlying debris flow initiation. In contrast, watershed-based units are delineated based on geomorphological features, reflecting the natural evolution of the environment and the differentiated boundaries between geomorphology and geology. This makes them more consistent with the actual developmental characteristics of debris flows [44; 45; 46]. Therefore, in this study, watershed units were extracted using GIS-based spatial analysis techniques applied to a Digital Elevation Model (DEM). By dynamically adjusting the catchment area threshold based on high-resolution remote sensing imagery, a total of 1433 debris flow susceptibility evaluation units were delineated.  
Research indicates that susceptibility assessment performs optimally when the number of hazard points and non-hazard points is balanced [47]. To construct the sample dataset, several watershed units were randomly selected, excluding confirmed positive samples. From the remaining units, rigorous validation via visual interpretation of high-resolution satellite imagery was conducted to ensure the absence of debris flow morphological features, severe erosion signs, or historical disaster traces. Additionally, a buffer distance was maintained between these points and known hazard sites to avoid potential active zones. Ultimately, 44 non-hazard watershed units were identified as negative samples. This comprehensive approach ensured that the selected non-hazard points represented stable areas with extremely low debris flow susceptibility in the study region.  
This resulted in a dataset comprising 88 samples for debris flow susceptibility analysis (Figure 4). The sample dataset was divided into training (70%) and validation (30%) subsets. To mitigate the risk of overfitting due to the limited sample size, which could restrict the effective utilization of data [48; 49], ten-fold spatial clustering cross-validation was applied to the training data, with model hyperparameters optimized accordingly [50]. Spatial clustering was performed using the K-means algorithm [51], which provides better spatial transferability compared to random splitting [52].  
Figure 4: Watershed unit division.