  A~machine learning framework is developed to estimate ocean-wave conditions.
By supervised training of machine learning models on many thousands of
iterations of a physics-based wave model, accurate representations of
significant wave heights and period can be used to predict ocean conditions. A
model of Monterey Bay was used as the example test site; it was forced by
measured wave conditions, ocean-current nowcasts, and reported winds. These
input data along with model outputs of spatially variable wave heights and
characteristic period were aggregated into supervised learning training and
test data sets, which were supplied to machine learning models. These machine
learning models replicated wave heights with a root-mean-squared error of 9cm
and correctly identify over 90% of the characteristic periods for the test-data
sets. Impressively, transforming model inputs to outputs through matrix
operations requires only a fraction (<1/1,000) of the computation time compared
to forecasting with the physics-based model.
