Title 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
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.
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