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

Áreas de investigación:

• Methodology - Machine learning

Año:

2016

Tipo de publicación:

Artículo en conferencia

Autores:

• Durán-Rosal, Antonio Manuel
• Fernández, Juan Carlos
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Título del libro:

2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016)

Páginas:

1-8

Organización:

Athens, Greece

Mes:

6th-9th December

ISBN:

978-1-5090-4240-1

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.