Deep Wave Height Prediction for Alexandria Sea Region by Using Nonlinear Regression Method Compared to Support Vector Machine

Elgohary, T.; Elbisy, Moussa S.; Mobasher, Amir; Salah, Hassan

doi:10.17654/oc010010001

Cited by 5 publications

(1 citation statement)

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“…While Malekmohamadi et al [11], Londhe et al [12], and Deshmukh et al [13] combined NN and numerical models to realize the wave height prediction, Sadeghifar et al [14] used recurrent neural networks (RNN) for wave predictions based on the data gathered and the measurement of the sea waves in the Caspian Sea in the north of Iran. Additionally, Elgohery et al [15] used nonlinear regression and SVM methods to predict significant wave height. The results explained that the use of nonlinear regression methods gave a good result as compared to the results from the SVM; However, the results also indicated that the SVM based on radial basis function is more superior to the nonlinear regression methods.…”

Section: Introductionmentioning

confidence: 99%

Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction

Elbisy¹,

Osra²

2019

AJNNA

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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.

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