Enhanced Earthquake Magnitude Prediction Using Hybrid Machine Learning and Deep Learning Models

This study evaluates the performance of machine learning and hybrid deep learning models for predicting earthquake magnitudes using historical seismic data. Five models, including Random Forest (RF), ARIMA, Long Short-Term Memory (LSTM), CNN+LSTM, and Transformer + Gaussian Processes (GP), were compared using metrics such as Root Mean Squared Error (RMSE) and R2. The RF model was quite efficient, with an RMSE of 0.072 and an R2 of 0.30. However, it did not incorporate temporal analysis. ARIMA was also better, with an RMSE of 0.065 and R2 of 0.42, which is best suited for linear relationships. LSTM identified the sequential relations well and provided an RMSE of 0.097 and R2 of 0.51. The hybrid CNN+LSTM model outperformed standalone approaches with an RMSE of 0.090 and R2 of 0.58 by combining spatial and temporal features. The Transformer + GP model achieved the highest accuracy, with an RMSE of 0.063 and R2 of 0.62, offering robust uncertainty quantification through confidence intervals. These results highlight the superiority of hybrid models in seismic forecasting, demonstrating their potential to improve predictive accuracy and support better risk management strategies.