Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction
American Journal of Neural Networks and Applications
Volume 5, Issue 2, December 2019, Pages: 51-57
Received: Oct. 5, 2019;
Accepted: Oct. 22, 2019;
Published: Oct. 28, 2019
Views 579 Downloads 120
Moussa Sobh Elbisy, Civil Engineering Department, Umm Al Qura University, Makkah, Saudi Arabia
Faisal Abdulrahman Osra, Civil Engineering Department, Umm Al Qura University, Makkah, Saudi Arabia
The estimation of wave parameters is of great importance in coastal activities such as design studies for harbor, inshore and offshore structures, coastal erosion, sediment transport, and wave energy estimation. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, numerical-based approaches, and soft computing. In this study, the group method of data handling type neural network (GMDH-NN) was presented for significant wave height prediction in an attempt to suggest a new model with superior explanatory power and stability. The GMDH-NN results were compared with the field data and with a multilayer perceptron neural networks (MLPNN) model. The results indicate that the prediction accuracy and avoidance of over-fitting of the GMDH-NN method were superior to those of the MLPNN method. The percentage improvement in the root mean square error and the mean absolute percentage error of the GMDH-NN model over the MLPNN model were 72.92% and 81.02%, respectively. Also, according to the indices, the GMDH-NN model performs the best for predicting the Hs of all of the wave height ranges. That is, the GMDH-NN model is capable of predicting wave heights for different ranges. The results of the analysis suggest that the GMDH-NN-based modeling is effective in predicting significant wave height.
Moussa Sobh Elbisy,
Faisal Abdulrahman Osra,
Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction, American Journal of Neural Networks and Applications.
Vol. 5, No. 2,
2019, pp. 51-57.
Deo, M. C., and Naidu, C. S. (1999). “Real time wave forecasting using neural networks.” Ocean Engineering, Vol. 26, pp. 191-203.
Deo, M. C.; Jha, A., Chaphekar, A. S., and Ravikant, K. (2001). “Neural networks for wave forecasting.” Ocean Engineering, Vol. 28, pp. 889–898.
Agrawal, J. D. and Deo, M. C. (2002). “On-line wave prediction.” Marine Structures, Vol. 15, pp. 57–74.
Tsai, C. P., Lin, C., and Shen, J. N. (2002). “Neural network for wave forecasting among multi-stations.” Ocean Engineering, Vol. 29, pp. 1683-1695.
Makarynskyy, O. (2004). “Improving wave predictions with artificial neural networks.” Ocean Engineering, Vol. 31, No. 5–6, pp. 709–724.
Makarynskyy, O., Pires-Silva, A. A., Makarynska, D., and Ventura-Soares, C. (2005). “Artificial neural networks in wave predictions at the west coast of Portugal.” Comput. Geosci., Vol. 31, No. 4, pp. 415–424.
Mandal, S., and Prabaharan, N. (2006). “Ocean wave forecasting using recurrent neural networks.” Ocean Engineering, Vol. 33, pp. 1401–1410.
Mahjoobi, J., Etemad-Shahidi, A., and Kazeminezhad, M. H. (2008). “Hindcasting of wave parameters using different soft computing methods.” Applied Ocean Research, Vol. 30, pp. 28–36.
Günaydın, K. (2008). “The estimation of monthly mean significant wave heights by using artificial neural network and regression methods.” Ocean Engineering, Vol. 35, pp. 1406–1415.
Elbisy, M. S. (2015). “Sea wave parameters prediction by support vector machine using a genetic algorithm.” Journal of Coastal Research, Vol. 31, No. 4, pp. 892-899.
Malekmohamadi, I., Ghiassia, R., and Yazdanpanah, M. J. (2008). “Wave hindcasting by coupling numerical model and artiﬁcial neural networks.” Ocean Engineering, Vol. 35, pp. 417–425
Londhe, S. N., and Panchang, V. (2016). “One-day wave forecasts based on artiﬁcial neural networks.” J. Atmos. Oceanic Tech- nol., Vol. 23, pp. 1593–1603.
Deshmukh, A. N., Deo, M. C., Bhaskaran, P. K., Balakrishnan Nair, T. M., and Sandhya K. G., (2016). “Neural-network-based data assimilation to improve numerical ocean wave forecast.” IEEE J. Oceanic Eng., Vol. 41, pp. 944–953.
Sadeghifar, T., Motlagh, M. N., Azad, M. T., and Mahdizadeh, M. M. (2017). “Coastal wave height prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea.” Marine Geodesy, Vol. 40, No. 2, pp. 454-465.
Elgohary, T., Elbisy, M. S., Mobasher, A. M., and Salah, H. (2018). “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.
Haykin, S. (1999). “Neural networks: A comprehensive foundation.” New Jersey, NJ: Prentice Hall.
Garg, V. (2014). “Inductive group method of data handling neural network approach to model basin sediment yield.” J Hydrol Eng, Vol. 20, No. 6, C6014002.
Kalantary, F., Ardalan, H, and Nariman-Zadeh, N. (2009). “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.
Amanifard, N., Nariman-Zadeh, N., Farahani, M. H., and Khalkhali, A. (2008). “Modeling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks,” Energy Conversion and Management, vol. 49, No. 10, pp. 2588–94.
Nariman-Zadeh, N., Darvizeh, A., Felezi, M. E., and Gharababaei, H. (2002). “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.
Comola, F., Lykke Andersen, T., Martinelli, L., Burcharth, H. F., and Ruol, P.(2014). “Damage pattern and damage progression on breakwater roundheads under multidirectional waves.” Coastal Engineering, Vol. 83, pp. 24–35.
Kim, S. W., and Suh, K. D. (2014). “Determining the stability of vertical breakwaters against sliding based on individual sliding distances during a storm.” Coastal Engineering, Vol. 94, pp. 90–101.