PREDICTION OF HYDRODYNAMIC COEFFICIENTS OF PERMEABLE PANELED BREAKWATER USING ARTIFICIAL NEURAL NETWORKS
Hagras, Mona;
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.
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.
Other data
Title | PREDICTION OF HYDRODYNAMIC COEFFICIENTS OF PERMEABLE PANELED BREAKWATER USING ARTIFICIAL NEURAL NETWORKS | Authors | Hagras, Mona | Keywords | Artificial Neural Network; Wave transmission; Wave reflection; Permeable paneled breakwater | Issue Date | Aug-2013 | Publisher | International Journal of Engineering Science and Technology (IJEST) | Journal | International Journal of Engineering Science and Technology (IJEST) |
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