1. Hybridization of neural network models for the prediction of Extreme Significant Wave Height segments
- Author
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Antonio Manuel Durán-Rosal, Juan Carlos Fernández, César Hervás-Martínez, and Pedro Antonio Gutiérrez
- Subjects
010504 meteorology & atmospheric sciences ,Artificial neural network ,Computer science ,business.industry ,Evolutionary algorithm ,Basis function ,02 engineering and technology ,Machine learning ,computer.software_genre ,01 natural sciences ,Wave height ,Genetic algorithm ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Segmentation ,Artificial intelligence ,Time series ,Significant wave height ,business ,Algorithm ,computer ,0105 earth and related environmental sciences - 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.
- Published
- 2016
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