## 5 Comprehensive Analysis of Model Robustness, Generalizability, and Interpretability
### Analysis of Regional Transferability and Model Adaptability  
To evaluate the transferability and generalization performance of the model across regional datasets, this study introduced earthquake samples from Japan for external testing and validation. Basic earthquake parameters (magnitude, focal depth, epicentral intensity, etc.) were sourced from the USGS Earthquake Catalog, while socioeconomic factors such as fatality counts and population density were obtained from the NCEI Significant Earthquake Database. Ultimately, 27 Japanese earthquake samples were collected and organized, with relevant features presented in Table 13.  
First, the PCA-IWOA-XGBoost model, trained on Chinese earthquake data, was used to predict the death toll for these 27 samples. The results showed an \(R^{2}\) of only 0.55, indicating that the model could only explain 55% of the variance in the Japanese data; an _RMSE_ as high as 135 and an _MAE_ of 95, significantly exceeding the actual value range; and an _RPD_ of only 2.5 (below the empirical threshold of 3), suggesting low prediction accuracy and limited generalization ability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold** & **XGBoost** & **PCA-XGBoost** & **IWOA-XGBoost** & **PCA-IWOA-XGBoost** \\ \hline
1 & 35.6 & 25.3 & 15.7 & 8.12 \\
2 & 34.1 & 24.9 & 16.3 & 7.95 \\
3 & 36.2 & 25.9 & 15.9 & 8.22 \\
4 & 35.2 & 24.8 & 16.1 & 7.78 \\
5 & 34.3 & 24.3 & 17.2 & 7.96 \\
6 & 37.1 & 26.2 & 15.8 & 8.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Six-fold _RMSE_ values of each model. The above results indicate that while the PCA-IWOA-XGBoost model performed well on Chinese earthquake data, its predictive performance significantly declined when applied to Japanese samples. The root cause of this lies in the substantial differences between Chinese and Japanese earthquake events in several key influencing factors (e.g., building codes, population density, and urban-rural distribution structures), leading to a shift in the feature space between the two datasets. Since the principal component extraction was trained on Chinese samples, its weight structure struggled to adequately capture the social structures and disaster mitigation characteristics of Japanese earthquake events, resulting in a significant increase in prediction errors. To address the issue of poor model transferability, this study retrained the PCA-IWOA-XGBoost model using local Japanese samples and evaluated its performance with 6-fold cross-validation. The results showed a significant improvement in predictive accuracy on the Japanese dataset: \(R^{2}\) increased to 0.97, _RMSE_ decreased to 6.32, _MAE_ dropped to 5.63, and _RPD_ reached 21.41, comprehensively outperforming the directly transferred model. These findings demonstrate that PCA effectively extracted principal components tailored to Japanese earthquake characteristics, and the IWOA optimization strategy significantly enhanced the parameter adaptability of XGBoost. A comprehensive comparison revealed that while the PCA-IWOA-XGBoost model exceled on Chinese earthquake samples, its prediction errors increased markedly when directly transferred to Japanese samples, highlighting the model's sensitivity to regional feature differences. The performance improvement after retraining with local samples further underscored the model's strong learning capability. However, significant regional differences in factors such as building codes and population density limit the model's cross-regional generalization ability, emphasizing the necessity of developing localized, region-specific models for earthquake disaster prediction.