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Hybridization of neural network models for the prediction of extreme significant wave height segments

Authors

DURAN ROSAL, ANTONIO MANUEL, Fernandez, Juan C. , Gutierrez, Pedro A. , Hervas-Martinez, Cesar , IEEE

External publication

Si

Scope

Proceedings Paper

Nature

Científica

JCR Quartile

✗

SJR Quartile

✗

Publication date

01/01/2016

ISI

000400488302050

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 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.