This work proposes a hybrid methodology for the
detection and prediction of Extreme Significant Wave Height
(ESWH) periods in oceans. In a first step, wave height time
series is approximated by a labeled sequence of segments, which
is obtained using a genetic algorithm in combination with
a likelihood-based segmentation (GA+LS). Then, an artificial
neural network classifier with hybrid basis functions is trained
with a multiobjetive evolutionary algorithm (MOEA) in order
to predict the occurrence of future ESWH segments based on
past values. The methodology is applied to a buoy in the Gulf of
Alaska and another one in Puerto Rico. The results show that
the GA+LS is able to segment and group the ESWH values, and
the neural network models, obtained by the MOEA, make good
predictions maintaining a balance between global accuracy and
minimum sensitivity for the detection of ESWH events. Moreover,
hybrid neural networks are shown to lead to better results than
pure models
