Título Hybridization of neural network models for the prediction of extreme significant wave height segments
Autores DURAN ROSAL, ANTONIO MANUEL, Fernandez, Juan C. , Gutierrez, Pedro A. , Hervas-Martinez, Cesar , IEEE
Publicación externa Si
Alcance Proceedings Paper
Naturaleza Científica
Fecha de publicacion 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.
Miembros de la Universidad Loyola

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