| 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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