Abstract
This paper proposes an accurate hybrid method based on support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to predict the tidal current speed and direction. In the proposed hybrid model, the ARIMA model captures the linear component of the tidal current, and the remained residual components are modeled by SVR. In order to capture the maximum linear components, the appropriate order of the ARIMA model is determined by the Akaike information criterion. Autocorrelation and partial autocorrelation functions are used to verify the stationary or nonstationary characteristics of tidal data. In order to adjust the optimal values of SVR parameters, a new optimization method based on the crow search algorithm is developed to search the problem space globally. In addition, a three-phase modification method is proposed to increase the diversity of the algorithm. The proposed hybrid model is highly accurate and outperforms the artificial neural network, ARIMA, genetic algorithm, and SVR. This model is applied on the practical data collected from the Bay of Fundy, NS, Canada, in 2008.
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