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
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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.
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