High-precision earthquake motion prediction models contribute to the improvement of earthquake disaster early warning and response capabilities.Traditional regression methods for constructing earthquake motion prediction models rely on predefined equations,which have certain limitations and struggle to capture the complex patterns of seismic wave propagation.As a result,more and more scholars have been exploring the application of machine learning methods in constructing earthquake motion prediction models.However,using a single machine learning algorithm makes it difficult to capture more patterns from the data,leading to limited improvement in model accuracy.In this study,we utilized ground motion records collected from the KiK-net and K-Net strong motion networks in Japan.This study employed a Stacking model fusion strategy,with LightGBM,XGBoost,and CatBoost algorithms as base estimators and linear regression as the meta-estimator.Additionally,an objective and efficient Bayesian optimization algorithm was introduced to optimize the model's hyperparameters.The proposed approach resulted in a ground motion prediction fusion model called Stacking-Interface,specifically designed for subduction interface earthquakes in Japan.Comparative analysis of the proposed model,single machine learning models,and traditional models revealed that machine learning models generally outperformed traditional models in terms of accuracy.Furthermore,the fusion model demonstrated improved predictive capabilities compared to single machine learning models.The reliability and generalization ability of the proposed model was validated through comparisons with actual ground motion records and sensitivity analysis of feature parameters.The research methodology and results presented in this paper can serve as a reference for earthquake risk analysis.
维普
万方数据