## 3 Results
### Heavy Rain Disaster Risk Assessment
#### 3.2.5 SHAP Algorithm Interpretation Results  
To make the decision-making process of the MAML-RF model transparent and identify key influencing factors, this study employs the SHAP algorithm to calculate the Shapley values of each feature, thereby quantifying the impact of risk factors on heavy rain disaster risk. The results of the impact proportions of risk factors are presented in Table 6. Among the factors, the number of heavy rain days, rainfall amount, and slope have the greatest impact on heavy rain disasters, followed by factors such as drainage pipe density and impervious surface ratio. This is likely due to the process of surface runoff formation during heavy rain events. The longer the duration and the greater the rainfall, combined with an uneven spatial distribution of drainage pipes, results in delayed infiltration and drainage of rainwater. This leads to a series of disaster issues such as urban waterlogging, rapid river level rise, and soil erosion. The Beijing-Tianjin-Hebei region has an elevation difference of approximately 3000 m, featuring diverse terrains including plains and mountains. For instance, the Juma River's upstream is located in the Fangshan mountainous area with an elevation exceeding 1 km, while the downstream Zhuozhou City is just over 30 m above sea  
Figure 6: The confusion matrix of the model on the test set: (**a**) SCV-RF model; (**b**) MAML-RF model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \hline Evaluation & SCV-RF & MAML-RF & SCV-RF & MAML-RF \\ accuracy & 91.35\% & 92.31\% & 86.67\% & 91.11\% \\ Precision & 93.50\% & 94.74\% & 88.20\% & 91.91\% \\ Recall & 91.35\% & 92.31\% & 86.67\% & 91.11\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation accuracy of the SCV-RF model and MAML-RF model on the training set and test set respectively. level. During heavy rain, Zhuozhou City, situated downstream, directly faces the impact of high-speed, high-volume water flow from the upstream mountains. Due to its low and flat terrain and limited surface infiltration and drainage capacity, the risk of secondary disasters such as waterlogging and mudslides significantly increases in this area during heavy rain. To further illustrate the impact of risk factors on the model's simulation results, the global interpretation results of the SHAP algorithm for risk factors are shown in Figure 7. Each point represents a feature of a data sample, illustrating the distribution of each factor's impact on the model's internal output results across all samples. The vertical axis represents the risk factors, ranked from top to bottom by their impact on disaster risk. The greener a circle is, the higher the feature value of that sample; the more purple, the lower the factor value. The horizontal axis represents the SHAP value for each factor. A positive SHAP value with a green sample indicates that the feature value has a positive effect on the output result, with larger sample values exerting a greater positive influence. The results indicate that within the four dimensions of the disaster risk system, disaster-inducing factors, disaster-prone environments, and disaster mitigation capacity have a significant driving effect on regional heavy rain disaster risk. The primary driving factors include the number of heavy rain days, rainfall amount, slope, drainage pipe density, and impervious surface ratio, with changes in these characteristics having a substantial impact on risk. Specifically, the number of heavy rain days, rainfall amount, slope, and impervious