Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

Abstract

The accurate prediction of the mean wave overtopping rate at breakwaters is vital for a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural networks (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods, namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from the EurOtop database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated, the results of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon. HIGHLIGHTS Gaussian process regression (GPR) and support vector regression (SVR) methods were employed to predict the mean wave overtopping rate at simple sloped breakwaters.; The performances of GPR and SVR models were compared with those of ANN model and existing empirical formulae.; GPR and SVR models showed better performances compared to those of the ANN model and empirical formulae.; The optimal input combination with fewer number of input parameters, extracted from sensitivity analysis, and high accuracy was introduced.; Physical consistency of developed GPR and SVR models were investigated based on the observed trend between the most effective input parameter and mean wave overtopping rate.;

Keywords

• ard-mattern5/2-gaussian process regression (gpr)
• ffbp-artificial neural network (ann)
• kernel-based models
• mean wave overtopping
• rbf-support vector regression (svr)
• simple sloped breakwaters