## 5 Comprehensive Analysis of Model Robustness, Generalizability, and Interpretability
### Model Interpretability Analysis Based on SHAP  
To enhance the model's interpretability, this study introduced the SHAP (Shapley Additive Explanations) method to analyze the impact of features on earthquake death toll predictions from both global and local perspectives. Based on game theory, SHAP calculates the marginal contribution of each feature to a model's predicted values, providing consistent and theoretically grounded interpretability for tree-based models (e.g., XGBoost). Using the Python 3.8.9 shap library and its Tree Explainer module, this study computed the mean absolute SHAP values for principal components \(F_{1}\) to \(F_{6}\) to evaluate their importance in the overall predictions. The analysis results are presented in Figure 13 and Table 14.  
The global importance results indicate that \(F_{1}\) had the highest average SHAP value of 0.320, driven primarily by epicentral intensity (x\({}_{4}\), coefficient 0.301), magnitude (x\({}_{2}\), coefficient 0.270), and \(\beta\) value (x\({}_{6}\), coefficient 0.183). \(F_{3}\) followed with a SHAP value of 0.250, mainly composed of seismic fortification intensity (x\({}_{5}\)), focal depth (x\({}_{3}\)), and building codes (x8). \(F_{2}\), \(F_{5}\), \(F_{4}\), and \(F_{6}\) reflected secondary factors such as magnitude, population density, and occurrence time. This suggests that earthquake physical characteristics and disaster mitigation capabilities were the key drivers of the model's predictions. \begin{table}
\begin{tabular}{c c c c} \hline \hline \
\begin{tabular}{c} **Principal** \\ **Components** \\ \end{tabular} & **Mean SHAP Value** & **Importance Ranking** &
\begin{tabular}{c} **Contribution of Original Features (Based on** \\ **Coefficient Matrix)** \\ \end{tabular} \\ \hline \(F_{1}\) & 0.32 & 1 & Primarily driven by epicentral intensity (x\({}_{4}\), 0.301), magnitude (x\({}_{2}\), 0.27), and \(\beta\) (x\({}_{6}\), 0.183) \\ \(F_{3}\) & 0.25 & 2 & (x\({}_{5}\), 0.417), focal depth (x\({}_{3}\), 0.279), and building codes (x\({}_{8}\), 0.323) \\ \(F_{2}\) & 0.18 & 3 & Primarily driven by magnitude (x\({}_{2}\), 0.247), \(\beta\) (x\({}_{6}\), 0.224), and urban–rural classification (x\({}_{11}\), 0.23) \\ \(F_{5}\) & 0.12 & 4 & Primarily driven by focal depth (x\({}_{3}\), 0.654), population density (x\({}_{7}\), 0.515), and topography (x\({}_{9}\), 0.591) \\ \(F_{4}\) & 0.09 & 5 & Primarily driven by time of occurrence (x\({}_{1}\), 0.53) and seismic fortification intensity (x\({}_{5}\), 0.289) \\ \(F_{6}\) & 0.06 & 6 & Primarily driven by time of occurrence (x\({}_{1}\), 0.759) and building codes (x\({}_{8}\), 0.732) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: SHAP-based importance of principal components and their underlying feature contributions. Figure 13: Mean absolute SHAP values of principal components. To further elucidate the model's prediction mechanisms across different earthquake scenarios, two representative samples were selected for local SHAP force plot analysis: Sample 3 (actual death toll = 743), representing a high-fatality event, and Sample 5 (actual death toll = 11), representing a low-fatality event. For Sample 3, \(F_{1}\) (+0.280) and \(F_{3}\) (+0.200) exhibited significant positive contributions to the predicted value, reflecting the combined effects of high epicentral intensity, large magnitude, shallow focal depth, and inadequate fortification. \(F_{2}\) and \(F_{5}\) also contributed positively, but to a lesser extent. In contrast, for Sample 5, \(F_{1}\) (\(-\)0.200) and \(F_{3}\) (\(-\)0.150) significantly reduced the predicted value, reflecting the disaster mitigation effects of lower intensity and deeper focal depth, with \(F_{5}\) having a more limited impact. The comprehensive SHAP analysis revealed that principal components \(F_{1}\) and \(F_{3}\) consistently influenced the outcomes across different samples, serving as key factors determining the death toll, representing intrinsic earthquake attributes and regional disaster mitigation capabilities, respectively.