Hybrid Neural Network Model for Metocean Data Analysis - Abdulkadir, Said Jadid; Yong, Suet-Peng; Zakaria, Nordin (Journal of Informatics and Mathematical Sciences, 2016)
Metocean time-series data is generally classified as highly chaotic thus making the analysis process tedious. The main aim of forecasting Metocean data is to obtain an effective solution for offshore engineering projects, such analysis of environmental conditions is vital to the choices made during planning and operational stage which must be efficient and robust. This paper presents an empirical analysis of Metocean time-series using a hybrid neural network model by performing multi-step-ahead forecasts. The proposed hybrid model is trained using a gauss approximated Bayesian regulation algorithm. Performance analysis based on error metrics shows that proposed hybrid model provides better multi-step-ahead forecasts as in comparison to previously used models.