Machine learning-based tsunami inundation prediction derived from offshore observations
Iyan E Mulia1,2, Naonori Ueda3,4, Takemasa Miyoshi3,5
1Prediction Science Laboratory, RIKEN Cluster for Pioneering Research, Kobe, Japan. iyan.mulia@riken.jp.
Nature Communications
|September 19, 2022
Summary
This study introduces a machine learning model for real-time tsunami inundation prediction using Japan's extensive tsunami observing system. The model offers rapid, accurate forecasts, significantly reducing computational costs compared to traditional methods.
Area of Science:
- Earth Sciences
- Computer Science
- Oceanography
Background:
- Tsunami prediction accuracy is crucial for coastal safety.
- Conventional tsunami models require extensive computational resources and accurate source estimations.
- Real-time tsunami inundation prediction remains a significant challenge.
Purpose of the Study:
- To develop a machine learning-based method for real-time tsunami inundation prediction.
- To leverage data from the world's largest tsunami observing system for improved forecasting.
- To reduce computational costs and uncertainties associated with traditional tsunami modeling.
Main Methods:
- Utilized data from 150 offshore stations in the Japan Trench.
- Trained a machine learning model on 3093 hypothetical tsunami scenarios (Mw 8.0-9.1 megathrust and Mw 7.0-8.7 outer-rise earthquakes).
- Tested the model against 480 unseen scenarios and 3 historical tsunami events.
Main Results:
- The machine learning model achieved accuracy comparable to physics-based models.
- Demonstrated a ~99% reduction in computational cost.
- Successfully predicted tsunami inundation for seven coastal cities along the Sanriku coast.
Conclusions:
- Machine learning offers a rapid and computationally efficient approach to real-time tsunami inundation prediction.
- Direct use of offshore observations enhances forecast lead time and reduces source estimate uncertainties.
- The developed method provides a viable alternative for improving tsunami early warning systems.
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