DEEP LEARNING TO PREDICT TSUNAMI HEIGHT AT THE SHORELINE USING OCEAN BOTTOM PRESSURE DATA

Willington Renteria; Patrick Lynett

doi:10.9753/icce.v37.management.98

No. 37 (2022), Coastal Management, Environment, and Risk

No. 37 (2022)

DEEP LEARNING TO PREDICT TSUNAMI HEIGHT AT THE SHORELINE USING OCEAN BOTTOM PRESSURE DATA

Coastal Management, Environment, and Risk

Published 2023-09-01

Willington Renteria
Patrick Lynett

Willington Renteria

Patrick Lynett

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How to Cite

DEEP LEARNING TO PREDICT TSUNAMI HEIGHT AT THE SHORELINE USING OCEAN BOTTOM PRESSURE DATA. (2023). Coastal Engineering Proceedings, 37, management.98. https://doi.org/10.9753/icce.v37.management.98

Abstract

Real-time tsunami prediction is a required component of a tsunami warning system. Several advances have been made to improve prediction in the tsunami warning process, including precomputed databases and the assimilation of deep-ocean observations (DART buoys) into numerical modeling (Bernard and Titov, 2015). These improvements aim to accurately and quickly predict the time and height of the tsunami wave impact. Here, two deep learning models (DLM) are developed to predict the maximum tsunami height at a local/long shoreline from four time series observations of ocean bottom pressure data.

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References

Bernard, Titov (2015). Evolution of tsunami warning systems and products. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical, and Engineering Sciences, 373(2053), 20140371.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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DEEP LEARNING TO PREDICT TSUNAMI HEIGHT AT THE SHORELINE USING OCEAN BOTTOM PRESSURE DATA

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