| dc.description.abstract | This study utilizes the XGBoost model integrated with the Flatfile database, derived from seismic records of the Taiwan Strong Motion Instrumentation Program (TSMIP) and archived by the Taiwan Seismic Senior Hazard Analysis Committee (TWSSHAC) project, to establish crustal seismic models (GMM) for PGA, PGV, and spectral acceleration. When evaluating the performance of these models, particular attention is paid to the standard deviation of residuals and the R² score, as well as multiple analysis methods such as distance response, response spectra, and residual distribution to thoroughly assess the effectiveness and rationality of the models. Additionally, a key aspect of the methodology is the application of SHAP (SHapley Additive exPlanations) values to reveal the importance of different predictors within our model. This approach not only clarifies the impact of various factors on strong ground motion but also highlights the interpretability of the model, addressing the traditional demands of engineering seismology for model rationality and explainability.
Considering the imbalance in the earthquake dataset, especially in cases of larger-scale events and shorter distances, the SMOGN method is used for data augmentation. This method effectively balances the dataset, thereby enhancing the model′s ability to learn from rare but significant large-scale seismic events. The study also assesses the applicability of the model in seismic hazard analysis using the seismic hazard analysis software, Openquake, incorporating the strong motion attenuation model proposed in this study for seismic motion simulation. The incorporation of machine learning techniques into the development of GMM marks a significant step forward in the advancement of seismic hazard analysis in Taiwan. This research could have a substantial impact on earthquake preparedness strategies, infrastructure resilience planning, and public safety protocols, demonstrating the potential of machine learning in earthquake science. | en_US |