@conference{timeDuranSSCI2016,
author = "Antonio Manuel Dur{\'a}n-Rosal and Juan Carlos Fern{\'a}ndez and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "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 at the Gulf of Alaska and another one
at 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.",
booktitle = "2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016)",
doi = "10.1109/SSCI.2016.7850144",
isbn = "978-1-5090-4240-1",
month = "6th-9th December",
organization = " Athens, Greece",
pages = "1--8",
publisher = "IEEE Press",
title = "{H}ybridization of neural network models for the prediction of extreme significant wave height segments",
url = "dx.doi.org/10.1109/SSCI.2016.7850144",
year = "2016",
}