| dc.description.abstract | Taiwan is situated at a tectonic boundary, making it highly prone to severe earthquakes that often result in significant loss of life and substantial economic damage. If earthquake characteristics such as the time, location, and scale of earthquake events can be effectively predicted, it will help reduce the damage caused by them. Traditional earthquake prediction methods often rely on identifying precursors or anomalies before the events. However, this type of method is difficult to universally apply. In recent years, data-driven machine-learning approaches have shown more reliable results in predicting natural disasters. However, existing studies usually require complex data processing and the selection of seismic indicators. In response to these challenges, this study introduces a novel approach that directly utilizes the earthquake catalog. By developing three attention-based Bi-LSTM models that process raw historical earthquake data from 2002 to 2022 into multivariate time series data via a sliding window technique, this research aims to predict the time, magnitude, and location of upcoming earthquakes based on previously consecutive events. Time prediction was developed through regression-based learning, while the prediction of magnitude and location was implemented through classification-based learning. Numerical experiments were conducted to optimize hyperparameters, resulting in superior R^2 of the time prediction and F_1 scores for magnitude and location over previous models. Despite some susceptibility to overfitting due to the data imbalance, the results highlight the potential of using a straightforward approach to enhance earthquake prediction capabilities. This study not only advances earthquake prediction in Taiwan but also suggests a scalable model for other regions with similar seismic complexities. | en_US |