  In this paper, we propose and apply a deep learning strategy for the early
detection of the Draupner rogue (freak) wave, which is also known as the New
Year's wave. We use a long short term memory (LSTM) network and show that
Draupner rogue wave could have been observed at least minutes before the
catastrophically dangerous peak has appeared in the chaotic wave field using
the available data. Compared to the existing early warning times scales on the
order of seconds, this is a major step forward which will certainly enhance the
safety and understanding of the marine engineering. As the rogue wave data sets
get improved in the future, our results may be enhanced to increase the early
warning time scales. Our results can be used to predict other rogue waves and
extreme time-series phenomena in fields including but are not limited to
hydrodynamics and marine engineering, optics, finance, and Bose-Einstein
condensation, just to name a few.
