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Machine learning-based tsunami inundation prediction derived from offshore observations

Earth Sciences

Machine learning-based tsunami inundation prediction derived from offshore observations

I. E. Mulia, N. Ueda, et al.

This groundbreaking study, conducted by Iyan E. Mulia, Naonori Ueda, Takemasa Miyoshi, Aditya Riadi Gusman, and Kenji Satake, pioneers a real-time tsunami inundation prediction method leveraging machine learning and North Japan’s S-net data. With an astounding 99% reduction in computational costs, this model provides vital lead time in forecasts and addresses uncertainties in tsunami source estimations.

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Playback language: English
Abstract
This study develops a real-time tsunami inundation prediction method using machine learning and data from Japan's extensive offshore tsunami observing system (S-net). The model, trained on 3093 hypothetical tsunami scenarios and tested on 480 unseen scenarios and three historical events, achieves accuracy comparable to physics-based models with a 99% reduction in computational cost. Direct use of offshore observations increases forecast lead time and eliminates uncertainties from tsunami source estimations.
Publisher
Nature Communications
Published On
Sep 19, 2022
Authors
Iyan E. Mulia, Naonori Ueda, Takemasa Miyoshi, Aditya Riadi Gusman, Kenji Satake
Tags
tsunami prediction
machine learning
computational cost
forecast lead time
data assimilation
Japan
S-net

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