The Use of Group Method of Data Handling and Multilayer Perceptron Neural Network for the Prediction of Significant Wave Height

  • Moussa S. Elbisy Civil Engineering Dept., College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, Saudi Arabia
Keywords: Prediction, Group Method of Data Handling, multilayer perceptron, significant wave height

Abstract

The prediction of significant wave height is important in the planning, design, and operation of coastal and ocean structures. Although several empirical methods, numerical models, and soft-computing techniques to forecast wave parameters have been investigated, such forecasting still remains a complex problem in the field of ocean engineering. This study uses the group method of data handling-type neural network (GMDH-NN) and multilayer perceptron neural network (MLPNN) to predict significant wave height. Among the used models, the GMDH-NN is found to provide the best generalization capability and the lowest prediction error; therefore, this is the method that can be most successfully used to predict significant wave height.

References

. F. Comola, T. Lykke Andersen, L. Martinelli, H.F. Burcharth, and P. Ruol, “Damage Pattern and Damage Progression on Breakwater Roundheads under Multidirectional Waves.” Coastal Engineering, Vol. 83, pp. 24–35, 2014.

. S.W. Kim, and K.D. Suh “Determining the Stability of Vertical Breakwaters Against Sliding Based on Individual Sliding Distances during a Storm.” Coastal Engineering, Vol. 94, pp. 90–101, 2014.

. M.C. Deo, and C.S. Naidu “Real Time Wave Forecasting Using Neural Networks.” Ocean Engineering, Vol. 26, pp. 191-203, 1999.

. M.C. Deo, A. Jha, A.S. Chaphekar, and K. Ravikant “Neural Networks for Wave Forecasting.” Ocean Engineering, Vol. 28, pp. 889–898, 2001.

. J. D. Agrawal, and M. C. Deo “On-line wave prediction.” Marine Structures, Vol. 15, pp. 57–74, 2002.

. C.P. Tsai, C. Lin, and J. N. Shen “Neural Network for Wave Forecasting among Multi-Stations.” Ocean Engineering, Vol. 29, pp. 1683-1695, 2002.

. O. Makarynskyy “Improving Wave Predictions with Artificial Neural Networks.” Ocean Engineering, Vol. 31, No. 5–6, pp. 709–724, 2004.

. O. Makarynskyy, A.A. Pires-Silva, D. Makarynska, and C. Ventura-Soares “Artificial Neural Networks in Wave Predictions at the West Coast of Portugal.” Comput. Geosci., Vol. 31, No. 4, pp. 415–424, 2005.

. S. Mandal, and N. Prabaharan “Ocean Wave Forecasting Using Recurrent Neural Networks.” Ocean Engineering, Vol. 33, pp. 1401–1410, 2006.

. J. Mahjoobi, A. Etemad-Shahidi, and M.H. Kazeminezhad “Hindcasting of Wave Parameters Using Different Soft Computing Methods.” Applied Ocean Research, Vol. 30, pp. 28–36, 2008.

. K. Günaydın “The Estimation of Monthly Mean Significant Wave Heights by Using Artificial Neural Network and Regression Methods.” Ocean Engineering, Vol. 35, pp. 1406–1415, 2008.

. M.S. Elbisy “Sea Wave Parameters Prediction by Support Vector Machine Using a Genetic Algorithm.” Journal of Coastal Research, Vol. 31, No. 4, pp. 892-899, 2015.

. I. Malekmohamadi, R. Ghiassia, and M.J. Yazdanpanah “Wave Hindcasting by Coupling Numerical Model and Artificial Neural Networks.” Ocean Engineering, Vol. 35, pp. 417–425, 2008.

. S.N. Londhe, and V. Panchang “One-Day Wave Forecasts Based on Artificial Neural Networks.” J. Atmos. Oceanic Tech- nol., Vol. 23, pp. 1593–1603, 2016.

. A.N. Deshmukh, M.C. Deo, P.K. Bhaskaran, T.M. Balakrishnan Nair, and K.G.Sandhya “Neural-Network-Based Data Assimilation to Improve Numerical Ocean Wave Forecast.” IEEE J. Oceanic Eng., Vol. 41, pp. 944–953, 2016.

. T. Sadeghifar, M.N. Motlagh, M.T. Azad, and M.M. Mahdizadeh, “Coastal Wave Height Prediction Using Recurrent Neural Networks (RNNs) in the South Caspian Sea.” Marine Geodesy, Vol.40, No. 2, pp. 454-465, 2017.

. T. Elgohary, M.S. Elbisy, A.M. Mobasher, and H. Salah “Deep Wave Height Prediction for Alexandria Sea Region by Using Nonlinear Regression Method Compared to Support Vector Machines.” Current Development in Oceanography, Vol. 10, No. 1, pp. 1-14, 2018.

. V. Garg “Inductive Group Method of Data Handling Neural Network Approach to Model Basin Sediment Yield.” J Hydrol Eng, Vol. 20, No. 6, C6014002, 2014.

. N. Nariman-Zadeh, A. Darvizeh, M.E. Felezi, and H. Gharababaei “Polynomial Modelling of Explosive Compaction Process of Metallic Powders Using GMDH-type Neural Networks and Singular Value Composition.” Model. Simul. Mater. Sci. Eng., Vol. 10, No. 6, pp. 727-744, 2002.

. F. Kalantary, H. Ardalan, and N. Nariman-Zadeh “An investigation on the Su–NSPT Correlation Using GMDH Type Neural Networks and Genetic Algorithms.” Eng Geol, Vol. 104, No. 1–2, pp. 44–55, 2009.

Published
2019-10-23
Section
Articles