Prediction of Transmission Coefficients of Regular Wave Attenuation by Emergent Vegetation

Transmission coefficient ( K t ) for wave attenuation by vegetation is essential parameter for predicting the wave height. In this paper, based on the experimental data of three kind of artificial vegetation model, genetic programming (GP), artificial neural networks (ANNs) and multivariate non-linear regression (MNLR) were used to analyze the dimensionless factors including Ursell number ( Ur ), relative width ( R B ) relative height ( α ) and volume fraction ( φ ). The proposed GP formulae were compared with MNLR and ANNs. The predictions of GP models were in good agreement with measured data, and outperformed MNLR equations. Otherwise, GP and ANNs were used to obtain the weight of each factor. The results can provide a reference for the artificial planting of the three plants.