Advanced Earthquake Magnitude Prediction Using Regression and Convolutional Recurrent Neural Networks

Advanced Earthquake Magnitude Prediction Using Regression and Convolutional Recurrent Neural Networks

magnitude prediction; CRNN; regression techniques; seismic data analysis; machine learning

Earthquake magnitude prediction is critical in seismology, with significant implications for disaster risk management and mitigation. This study presents a novel earthquake magnitude prediction model by integrating regression analysis with Convolutional Recurrent Neural Networks (CRNNs). It utilisesConvolutional Neural Networks (CNNs) for spatial feature extraction from 2-dimensional seismic signal images and Long Short-Term Memory (LSTM) networks to capture temporal dependencies. The innovative model architecture incorporates residual connections and specialisedregression techniques for sequential data. Validated against a comprehensive seismic dataset, the model achieves a Mean Squared Error (MSE) of 0.1909 and a Root Mean Squared Error (RMSE) of 0.4369, with a coefficient of determination of 0.79772. These metrics, alongside a correlation coefficient of 0.8980, demonstrate the model's accuracy andconsistency in predicting earthquake magnitudes, establishing its potential for enhancing seismic risk assessment and informing early warning systems

Asep Id Hadiana1*, Rifaz Muhammad Sukma2, Eddie Krishna Putra3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5922/965

Departmentof Informatics, Facultyof Science and Informatics, Universitas Jenderal Achmad Yani, Cimahi, Indonesia

29-08-2024

FAJAR BAGUS W

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