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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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