Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks

Fumiyasu Makinoshima (), Yusuke Oishi, Takashi Yamazaki, Takashi Furumura and Fumihiko Imamura
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Fumiyasu Makinoshima: Fujitsu Laboratories Ltd.
Yusuke Oishi: Fujitsu Laboratories Ltd.
Takashi Yamazaki: Fujitsu Laboratories Ltd.
Takashi Furumura: The University of Tokyo
Fumihiko Imamura: Tohoku University

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Rapid and accurate hazard forecasting is important for prompt evacuations and reducing casualties during natural disasters. In the decade since the 2011 Tohoku tsunami, various tsunami forecasting methods using real-time data have been proposed. However, rapid and accurate tsunami inundation forecasting in coastal areas remains challenging. Here, we propose a tsunami forecasting approach using convolutional neural networks (CNNs) for early warning. Numerical tsunami forecasting experiments for Tohoku demonstrated excellent performance with average maximum tsunami amplitude and tsunami arrival time forecasting errors of ~0.4 m and ~48 s, respectively, for 1,000 unknown synthetic tsunami scenarios. Our forecasting approach required only 0.004 s on average using a single CPU node. Moreover, the CNN trained on only synthetic tsunami scenarios provided reasonable inundation forecasts using actual observation data from the 2011 event, even with noisy inputs. These results verify the feasibility of AI-enabled tsunami forecasting for providing rapid and accurate early warnings.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22348-0

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DOI: 10.1038/s41467-021-22348-0

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