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