  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 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.
