Modeling Uncertainty in Tidal Current Forecast Using Prediction Interval-Based SVR

Abstract

This paper proposes a prediction interval-based model for modeling the uncertainties of tidal current prediction. The proposed model constructs the optimal prediction intervals (PIs) based on support vector regression (SVR) and a nonparametric method called a lower upper bound estimation (LUBE) method. In order to increase the modeling stability of SVRs that are used in the LUBE method, the idea of combined prediction intervals is employed. As the optimization tool, a flower pollination algorithm along with a two-phase modification method is presented to optimize the SVR parameters. The proposed model employs fuzzy membership functions to provide appropriate balance between the PI coverage probability (PICP) and PI normalized average width (PINAW), independently. The performance of the proposed model is examined on the practical tidal current data collected from the Bay of Fundy, NS, Canada.