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Machine learning-based tsunami inundation prediction derived from offshore observations

Author

Listed:
  • Iyan E. Mulia

    (RIKEN Cluster for Pioneering Research
    RIKEN Center for Advanced Intelligence Project)

  • Naonori Ueda

    (RIKEN Cluster for Pioneering Research
    RIKEN Center for Advanced Intelligence Project)

  • Takemasa Miyoshi

    (RIKEN Cluster for Pioneering Research
    RIKEN Center for Computational Science)

  • Aditya Riadi Gusman

    (GNS Science)

  • Kenji Satake

    (The University of Tokyo)

Abstract

The world’s largest and densest tsunami observing system gives us the leverage to develop a method for a real-time tsunami inundation prediction based on machine learning. Our method utilizes 150 offshore stations encompassing the Japan Trench to simultaneously predict tsunami inundation at seven coastal cities stretching ~100 km along the southern Sanriku coast. We trained the model using 3093 hypothetical tsunami scenarios from the megathrust (Mw 8.0–9.1) and nearby outer-rise (Mw 7.0–8.7) earthquakes. Then, the model was tested against 480 unseen scenarios and three near-field historical tsunami events. The proposed machine learning-based model can achieve comparable accuracy to the physics-based model with ~99% computational cost reduction, thus facilitates a rapid prediction and an efficient uncertainty quantification. Additionally, the direct use of offshore observations can increase the forecast lead time and eliminate the uncertainties typically associated with a tsunami source estimate required by the conventional modeling approach.

Suggested Citation

  • Iyan E. Mulia & Naonori Ueda & Takemasa Miyoshi & Aditya Riadi Gusman & Kenji Satake, 2022. "Machine learning-based tsunami inundation prediction derived from offshore observations," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33253-5
    DOI: 10.1038/s41467-022-33253-5
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    References listed on IDEAS

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    1. Fumiyasu Makinoshima & Yusuke Oishi & Takashi Yamazaki & Takashi Furumura & Fumihiko Imamura, 2021. "Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    2. J. Selva & S. Lorito & M. Volpe & F. Romano & R. Tonini & P. Perfetti & F. Bernardi & M. Taroni & A. Scala & A. Babeyko & F. Løvholt & S. J. Gibbons & J. Macías & M. J. Castro & J. M. González-Vida & , 2021. "Probabilistic tsunami forecasting for early warning," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
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