MODELLING WAVE REFLECTION BY IMPLEMENTING ARTIFICIAL NEURAL NETWORKS IN PORT AREAS

Boussinesq-type wave models (e.g. Shi et al., 2012) have been traditionally employed to simulate wave agitation in port basins, however they are often associated with significant computational burden. An alluring alternative, with respect to the accuracy and efficiency, concerns models based on the time-depended hyperbolic mild slope wave equation, However, the prescription of partial wave reflection in both the aforementioned model categories requires the definition of an eddy viscosity/ porosity coefficient which makes the pre-processing process a tedious task. In this paper the training and validation of an Artificial Neural Network (ANN), that enables the efficient and accurate definition of the appropriate eddy viscosity coefficients depending on the desirable reflection coefficient, is presented. The trained ANN is an integral part of an enhanced hyperbolic mild slope (Chondros et al., 2021) wave model (entitled HMS thereafter) able to simulate wave propagation inside ports and coastal areas.