Machine learning-based tsunami inundation prediction derived from offshore observations

Iyan E. Mulia (), Naonori Ueda, Takemasa Miyoshi, Aditya Riadi Gusman and Kenji Satake
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Iyan E. Mulia: RIKEN Cluster for Pioneering Research
Naonori Ueda: RIKEN Cluster for Pioneering Research
Takemasa Miyoshi: RIKEN Cluster for Pioneering Research
Aditya Riadi Gusman: GNS Science
Kenji Satake: The University of Tokyo

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract 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 (Mw 8.0–9.1) and nearby outer-rise (Mw 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.

Date: 2022
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DOI: 10.1038/s41467-022-33253-5

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