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The Predictability of the 30 October 2020 İzmir-Samos Tsunami Hydrodynamics and Enhancement of Its Early Warning Time by LSTM Deep Learning Network

  • Ali Rıza Alan*
  • , Cihan Bayındır*
  • , Fatih Ozaydin
  • , Azmi Ali Altintas
  • *Bu çalışma için yazışmadan sorumlu yazar
  • Istanbul Technical University
  • Bogazici University
  • Tokyo International University
  • Istanbul University

Araştırma çıktısı: Dergi yayını › Makale › Hakemli

22 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Makale numarası4195
DergiWater (Switzerland)
Hacim15
Basın numarası23
DOI'lar
Yayın durumuYayınlandı - Ara 2023

Bibliyografik not

Publisher Copyright:
© 2023 by the authors.

Finansman

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.

FinansörlerFinansör numarası
TÜBA
Tokyo International University
Türkiye Bilimler Akademisi
Istanbul Teknik ÜniversitesiMDK-2021-42849, MGA-2022-43528

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