A Combined Prognostic Model Based on Machine Learning for Tidal Current Prediction

Abstract

This paper proposes a univariate prognostic approach based on wavelet transform and support vector regression (SVR) to predict the tidal current speed and direction with high accuracy. The proposed model decomposes the tidal current data into some subharmonic components. The details and approximation components are later fed to several SVR models to attend the prediction process. In order to increase the robustness of the model, the idea of combined prediction is used to model each subharmonic signal by several SVRs. The median operator is further used to determine the aggregated forecast tidal current data. Due to the high reliance of SVR model on the kernel function and hyperplane parameters, a new optimization method based on the bat algorithm is used to train the SVR model. The final forecast tidal current data are constructed using an aggregation operator in the output of the SVRs. The accuracy and satisfying performance of the proposed model are examined on the practical tidal data collected from the Bay of Fundy, NS, Canada. The experimental results reveal the high capability and robustness of the proposed hybrid model for the tidal current prediction.