PREDICTION OF HYDRODYNAMIC COEFFICIENTS OF PERMEABLE PANELED BREAKWATER USING ARTIFICIAL NEURAL NETWORKS

Abstract

In the present study, Artificial Neural Networks (ANNs) with different topologies have been evaluated to be used to predict hydrodynamic coefficients of permeable paneled breakwater. Two neural network models are constructed, one to predict wave transmission coefficient (Kt) and another for the prediction of wave reflection coefficient (Kr). Back propagation algorithm was used to train a multi-layer feed-forward network (Levenberg Marquardt algorithm). The capability of ANN topologies to estimate these coefficients is evaluated using the Mean Squared Error (MSE). Based on training patterns of different ANNs, a 5-7-1 topology has been selected to predict both coefficients. The results of the developed ANN models proved that this technique is reliable in such field. A good match between the measured and predicted values was observed with correlation values varying in the range (0.9508-0.9805) for the training set and (0.9159-0.9877) for the testing set.