Data pre-processing and artificial neural networks for tidal level prediction at the Pearl River Estuary

Abstract

Abstract Traditionally, tidal level is predicted by harmonic analysis (HA). In this paper, three hybrid models that couple varied pre-processing methods, which are empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and empirical wavelet transform (EWT), with the nonlinear autoregressive networks with exogenous inputs (NARX) were applied to forecast tidal level. The models were, namely, EMD-NARX, EEMD-NARX, and EWT-NARX. The sub-series obtained by using EMD or EEMD or EWT were then used as the input vectors to the NARX with the original data as targets. Notably, the EWT-NARX model was employed to predict the tidal level for the first time. Simulations were based on the measurements from four tidal stations at the Pearl River Estuary, China. The results showed that the EWT-NARX, EEMD-NARX, and EMD-NARX outperformed the HA model. Specifically, EWT-NARX was optimal among the four. Moreover, from the Hilbert energy spectra we can see the EWT solved the mode-mixing problem that EMD and EEMD suffered from, thus enabling precise tidal level prediction. Simulations and experimental results confirmed that the EWT-NARX model can achieve prediction of the tidal level with high accuracy.