Hybrid Neural Network Model for Metocean Data Analysis

Said Jadid Abdulkadir, Suet-Peng Yong, Nordin Zakaria

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.


Keywords


Chaotic time-series, hybrid model, Metocean data

Full Text:

PDF

References


Abdulkadir, S.J., Yong, S.P., Marimuthu, M., Lai, F.W., Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting. In: Mining Intelligence and Knowledge Exploration,. Springer (2014), 72–81.

Abdulkadir, S.J., Yong, S.P., Unscented kalman filter for noisy multivariate financial time-series data. In: Multi-disciplinary Trends in Artificial Intelligence,. Springer (2013), 87–96.

Abdulkadir, S.J., Yong, S.P., Scaled ukf–narx hybrid model for multi-step-ahead forecasting of chaotic time series data. Soft Computing 19, (2015), 3479–3496.

Ardalani-Farsa, M., Zolfaghari, S., Chaotic time series prediction with residual analysis method using hybrid elman–narx neural networks. Neurocomputing 73(13), (2010), 2540–2553.

Chen, P.A., Chang, L.C., Chang, F.J., Reinforced recurrent neural networks for multi-step-ahead flood forecasts. Journal of Hydrology 497, (2013), 71–79.

Foley, A.M., Leahy, P.G., Marvuglia, A., McKeogh, E.J., Current methods and advances in forecasting of wind power generation. Renewable Energy 37(1), (2012), 1–8.

Jacobsen, V., Rugbjerg, M., Offshore wind farms–the need for metocean data. Copenhagen Offshore Wind (2005).

Khashei, M., Bijari, M., A novel hybridization of artificial neural networks and arima models for time series forecasting. Applied Soft Computing 11(2), (2011), 2664–2675.

Khashei, M., Bijari, M., A new class of hybrid models for time series forecasting. Expert Systems with Applications 39(4), (2012), 4344–4357.

Montgomery, D.C., Jennings, C.L., Kulahci, M., Introduction to time series analysis and forecasting. John Wiley & Sons (2015).

Zhang, G.P., Neural networks for time-series forecasting. In: Handbook of Natural Computing, Springer (2012), 461–477.




DOI: http://dx.doi.org/10.26713%2Fjims.v8i4.555

eISSN 0975-5748; pISSN 0974-875X