Bayesian optimization of a hybrid system for robust ocean wave features prediction

In the last years, Bayesian optimization (BO) has emerged as a practical tool for high-quality parameter selection in prediction systems. BO methods are useful for optimizing black-box objective functions that either lack an analytical expression, or are very expensive to evaluate. In this paper, we show that BO can be used to obtain the optimal parameters of a prediction system for problems related to ocean wave features prediction. Specifically, we propose the Bayesian optimization of a hybrid Grouping Genetic Algorithm for attribute selection combined with an Extreme Learning Machine (GGA-ELM) approach for prediction. The system uses data from neighbor stations (usually buoys) in order to predict the significant wave height and the wave energy flux at a goal marine structure facility. The proposed BO methodology has been tested in a real problem involving buoys data in the Western coast of the USA, improving the performance of the GGA-ELM without a BO approach.