Hybrid Neural Network Model for Metocean Data Analysis

Authors

  • Said Jadid Abdulkadir High Performance Computing Center (HPCC), Universiti Teknologi Petronas, Tronoh
  • Suet-Peng Yong High Performance Computing Center (HPCC), Universiti Teknologi Petronas, Tronoh
  • Nordin Zakaria High Performance Computing Center (HPCC), Universiti Teknologi Petronas, Tronoh

DOI:

https://doi.org/10.26713/jims.v8i4.555

Keywords:

Chaotic time-series, hybrid model, Metocean data

Abstract

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.

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References

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Published

2016-12-31
CITATION

How to Cite

Abdulkadir, S. J., Yong, S.-P., & Zakaria, N. (2016). Hybrid Neural Network Model for Metocean Data Analysis. Journal of Informatics and Mathematical Sciences, 8(4), 245–251. https://doi.org/10.26713/jims.v8i4.555

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Section

Research Articles