Numerical simulation of wave propagation through rigid vegetation and a predictive model of drag coefficient using an artificial neural network

Coastal vegetation has been widely recognized as an effective nature-based measure for coastal protection. Numerous studies based on macroscopic models have been performed to simulate vegetation-induced wave attenuation. The modeling accuracy is closely related to a key input parameter, the drag coefficient (CD). To date, however, no reliable prediction method is available for determining CD, limiting the practical application of the macroscopic models. In this study, vegetation-induced wave attenuation was investigated using a macroscopic model that solved Reynolds-averaged Navier-Stokes equations by introducing a vegetation resistance force to account for momentum loss. A stabilized k-epsilon turbulence model considering vegetation effects was developed for turbulence closure. The volume-of-fluid technique was used to capture the free surface. Using a calibration method to determine CD, the numerical model was validated based on several laboratory experiments. A cor-relation analysis was performed to reveal the potential contributions of dimensionless parameters about waves and vegetation to the calibrated CD. Multivariate non-linear regression (MNLR) and artificial neural network (ANN) methods were adopted to develop a CD predictive model. Comparisons indicated that the prediction performance of ANN model is superior to that of the MNLR model. The ANN model has the potential as a promising predictive tool for obtaining CD when simulating wave propagation through rigid vegetation.