  We present a framework for forecasting significant wave height on the
Southwestern Atlantic Ocean using the long short-term memory algorithm (LSTM),
trained with the ERA5 database available through Copernicus Climate Data Store
(CDS) implemented by ECMWF (European Center for Medium Range Forecast) and also
with buoy data. The predictions are made for seven different locations in the
Brazilian coast, where buoy data are available, ranging from shallow to deep
water. Experiments are conducted using exclusively historical series at the
selected locations and the influence of other variables as inputs for training
is investigated. The results shows that a data-driven methodology can be used
as a surrogate to the computational expensive physical models, with the best
accuracy near $95\%$, compared to reanalysis
