Abstract
Although tsunamis occur less frequently compared to some other natural disasters, they can be extremely devastating in the nearshore environment if they occur. An earthquake of magnitude 6.9 Mw occurred on 30 October 2020 at 12:51 p.m. UTC (2:51 p.m. GMT+03:00) and its epicenter was approximately 23 km south of İzmir province of Turkey, off the Greek island of Samos. The tsunami event triggered by this earthquake is known as the 30 October 2020 İzmir-Samos (Aegean) tsunami, and in this paper, we study the hydrodynamics of this tsunami using some of these artificial intelligence (AI) techniques applied to observational data. More specifically, we use the tsunami time series acquired from the UNESCO data portal at different stations of Bodrum, Syros, Kos, and Kos Marina. Then, we investigate the usage and shortcomings of the Long Short Term Memory (LSTM) DL technique for the prediction of the tsunami time series and its Fourier spectra. More specifically we study the predictability of the offshore water surface elevation dynamics, their spectral frequency and amplitude features, possible prediction success and enhancement of the accurate early prediction time scales. The uses and applicability of our findings and possible research directions are also discussed.
Original language | English |
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Article number | 4195 |
Journal | Water (Switzerland) |
Volume | 15 |
Issue number | 23 |
DOIs | |
Publication status | Published - Dec 2023 |
Bibliographical note
Publisher Copyright:© 2023 by the authors.
Funding
This study is funded by the Turkish Academy of Sciences (TÜBA)-Outstanding Young Scientist Award (GEBİP), and the Research Fund of the Istanbul Technical University with project codes: MGA-2022-43528, MDK-2021-42849 and by the Personal Research Fund of Tokyo International University.
Funders | Funder number |
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TÜBA | |
Tokyo International University | |
Türkiye Bilimler Akademisi | |
Istanbul Teknik Üniversitesi | MDK-2021-42849, MGA-2022-43528 |
Keywords
- 30 October 2020 İzmir-Samos (Aegean) tsunami
- deep learning
- LSTM
- time series analysis and prediction