Machine learning-based tsunami inundation prediction derived from offshore observations

The world’s largest and densest tsunami observing system gives us the leverage to develop a method for a real-time tsunami inundation prediction based on machine learning. Our method utilizes 150 offshore stations encompassing the Japan Trench to simultaneously predict tsunami inundation at seven coastal cities stretching ~100 km along the southern Sanriku coast. We trained the model using 3093 hypothetical tsunami scenarios from the megathrust ( M w 8.0–9.1) and nearby outer-rise ( M w 7.0–8.7) earthquakes. Then, the model was tested against 480 unseen scenarios and three near-field historical tsunami events. The proposed machine learning-based model can achieve comparable accuracy to the physics-based model with ~99% computational cost reduction, thus facilitates a rapid prediction and an efficient uncertainty quantification. Additionally, the direct use of offshore observations can increase the forecast lead time and eliminate the uncertainties typically associated with a tsunami source estimate required by the conventional modeling approach. One of the main challenges in the tsunami inundation prediction is related to the real-time computational efforts done under restrictive time constraints. Here the authors show that using machine learning-based model, we can achieve comparable accuracy to the physics-based model with ~99% computational cost reduction.