Integrating Epidemic-Type Aftershock Sequence (ETAS) model and multi-output deep learning for forecasting seismicity rate and high-risk zones in Sumatra

Earthquake prediction remains a key challenge in disaster mitigation, particularly in seismically active regions like Sumatra. For predictive purposes, the Epidemic-Type Aftershock Sequence (ETAS) model has recently been combined with a neural network. However, two main issues arise: (1) the difficulty to tune a number of hyperparameters within the neural network models and (2) an impractical separated procedure. In this study, we incorporate Evolutionary Neural Architecture Search (ENAS) to obtain optimal hyperparameters and consider multi-output deep learning architectures to perform simultaneous forecasts of maximum seismicity rate and highest-risk geographical zone in the Sumatra region of Indonesia. The results indicate improvements in predictive performance relative to conventional deep learning techniques, featuring improved accuracy and computational efficiency. These findings emphasize the promise of ENAS and multi-output architectures in enhancing seismic forecasting, offering practical insights for disaster preparedness and risk reduction in earthquake-prone areas.