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

Fumiyasu Makinoshima, Yusuke Oishi, Takashi Yamazaki, Takashi Furumura, Fumihiko Imamura

研究成果: Article査読

抄録

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.

本文言語English
論文番号2253
ジャーナルNature communications
12
1
DOI
出版ステータスPublished - 2021 12

ASJC Scopus subject areas

  • 化学 (全般)
  • 生化学、遺伝学、分子生物学(全般)
  • 物理学および天文学(全般)

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