Bayındır C.

Turkish World Mathematical Society Journal of Applied and Engineering Mathematics, vol.13, no.1, pp.373-385, 2023 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 1
  • Publication Date: 2023
  • Journal Name: Turkish World Mathematical Society Journal of Applied and Engineering Mathematics
  • Journal Indexes: Scopus
  • Page Numbers: pp.373-385
  • Keywords: Deep learning, Long short term memory, Oceanic current and circulations, Predictability of oceanic circulations, Spectral properties of oceanic current
  • Istanbul Technical University Affiliated: Yes


© Isik University, Department of Mathematics, 2023; all rights reserved.In this paper, we analyze the predictability of the ocean currents using deep learning. More specifically, we apply the Long Short Term Memory (LSTM) deep learning network to a data set collected by the National Oceanic and Atmospheric Administration (NOAA) in Massachusetts Bay between November 2002-February 2003. We show that the current speed in two horizontal directions, namely u and v, can be predicted using the LSTM. We discuss the effect of training data set on the prediction error and on the spectral properties of predictions. Depending on the temporal or the spatial resolution of the data, the prediction times and distances can vary, and in some cases, they can be very beneficial for the prediction of the ocean current parameters. Our results can find many important applications including but are not limited to predicting the statistics and characteristics of tidal energy variation, controlling the current induced vibrations of marine structures and estimation of the wave blocking point by the chaotic oceanic current and circulation.