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

Author:

Alan Ali Rıza1ORCID,Bayındır Cihan12ORCID,Ozaydin Fatih3ORCID,Altintas Azmi Ali4ORCID

Affiliation:

1. Engineering Faculty, İstanbul Technical University, İstanbul 34469, Turkey

2. Engineering Faculty, Boğaziçi University, İstanbul 34342, Turkey

3. Institute for International Strategy, Tokyo International University, 4-42-31 Higashi-Ikebukuro, Toshima-ku, Tokyo 170-0013, Japan

4. Department of Physics, Faculty of Science, İstanbul University, Vezneciler, İstanbul 34116, Turkey

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.

Funder

Turkish Academy of Sciences (TÜBA)-Outstanding Young Scientist Award

Research Fund of the Istanbul Technical University

Personal Research Fund of Tokyo International University

Publisher

MDPI AG

Subject

Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry

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