The Predictability of the 30 October 2020 İzmir-Samos Tsunami Hydrodynamics and Enhancement of Its Early Warning Time by LSTM Deep Learning Network
Abstract
:1. Introduction
2. Methodology
2.1. Study Area
2.2. Review of the LSTM
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artifical Intelligence |
DART | Deep-ocean Assessment and Reporting of Tsunami |
DL | Deep Learning |
FFT | Fast Fourier Transform |
GMT | Greenwich Mean Time |
IFFT | Inverse Fast Fourier Transform |
KOERI | Boğaziçi University Kandilli Observatory and Earthquake Research Institute |
LSTM | Long-short term memory |
NOAA | National Oceanic and Atmospheric Administration |
RMSE | Root Mean Square Error |
RNN | Recurrent Neural Networks |
UTC | Universal Time Coordinated |
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Alan, A.R.; Bayındır, C.; Ozaydin, F.; Altintas, A.A. The Predictability of the 30 October 2020 İzmir-Samos Tsunami Hydrodynamics and Enhancement of Its Early Warning Time by LSTM Deep Learning Network. Water 2023, 15, 4195. https://doi.org/10.3390/w15234195
Alan AR, Bayındır C, Ozaydin F, Altintas AA. The Predictability of the 30 October 2020 İzmir-Samos Tsunami Hydrodynamics and Enhancement of Its Early Warning Time by LSTM Deep Learning Network. Water. 2023; 15(23):4195. https://doi.org/10.3390/w15234195
Chicago/Turabian StyleAlan, Ali Rıza, Cihan Bayındır, Fatih Ozaydin, and Azmi Ali Altintas. 2023. "The Predictability of the 30 October 2020 İzmir-Samos Tsunami Hydrodynamics and Enhancement of Its Early Warning Time by LSTM Deep Learning Network" Water 15, no. 23: 4195. https://doi.org/10.3390/w15234195