Abstract
Inspired by the development of machine-learning methods, this study employed artificial neural-network-based techniques using different algorithms—such as the multilayer perceptron, cascade correlation, and general regression neural networks (GRNN), and a support vector machine with radial-bias functionality—to estimate the wave-overtopping discharge at coastal structures featuring a straight slope “without a berm.” The newly developed EurOtop database was used in this study. The predictive performance of each model was assessed using six statistical features. In terms of predicting the wave-overtopping discharge, the GRNN yielded highly accurate results. The obtained results were compared with those of the previous models. An in-depth sensitivity analysis was conducted to determine the significance of each predictive variable. Moreover, sensitivity analysis was also conducted to select the optimal validation method for the GRNN model. The results showed both validation methods to be highly accurate, with the leave-one-out validation method outperforming the cross-validation method by a small margin.
•The ANN and SVM models were used to predict the wave-overtopping discharge.•The performances of different models were compared with those of previous ones.•The GRNN yielded highly accurate results.•A sensitivity analysis was conducted to determine the significance of the predictor variables.