Real-time forecasting of wave heights using EOF – wavelet – neural network hybrid model

Abstract

Recently, along with the development of data-driven models, artificial neural networks (ANN) have been used in ocean wave forecasting models. Hybridization of ANN with wavelet analysis or fuzzy logic approach has also been used. The wavelet and neural network hybrid models (WNN models) show better performance than ANN models. However, their accuracy decreases with increasing lead time because they do not consider the relation between wave and meteorological variables. Moreover, the WNN model has been developed to forecast the wave height at a single location where the past wave height data are available. To resolve these problems, in this paper, a hybrid model is developed by combining the empirical orthogonal function analysis and wavelet analysis with the neural network (abbreviated as EOFWNN model). The past wave height data at multiple locations and the past and future meteorological data in the surrounding area including the wave stations are used as input data. The model then forecasts the wave heights at the locations for various lead times. The developed model is employed to forecast the wave heights at eight wave observation stations in the coastal waters around the East/Japan Sea. The EOFWNN model is shown to perform better compared with the WNN model for all lead times regardless of the decomposition level of wavelet analysis. The EOFWNN model is proven to be a promising tool for forecasting wave heights at multiple locations where the past wave height data and the past and future meteorological data in the surrounding area are available.