Abstract
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.
Highlights
Waves are important factors in the planning and design of harbors, waterways, shore protection projects, and offshore structures, as well as for environmental impact assessments and hazard mitigation
The support vector machine (SVM) results were compared with the field data and back-propagation neural network (BPNN) and cascade-correlation neural network (CCNN) models, and the results indicated that the SVM with a radial basis function kernel provides the best generalization capability and the lowest prediction error
The results show that the group method of data handling (GMDH)-neural networks (NNs) model significantly reduces overall error
Summary
Waves are important factors in the planning and design of harbors, waterways, shore protection projects, and offshore structures, as well as for environmental impact assessments and hazard mitigation. The field observation of waves is generally difficult, and the numerical modelling of waves is costly and time consuming Various methods, such as empirical, numerical, and soft computing approaches, have been proposed in the literature for wave parameter prediction. Agrawal and Deo [3] predicted the wave heights using a back-propagation neural network (BPNN), a cascade-correlation neural network (CCNN), and autoregressive models (ARMA and ARIMA). These authors reported that the NNs were more accurate than the autoregressive methods. Mahjoobi, et al [8] compared NNs, FISs and ANFISs in hindcasting wave parameters
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