In this work, a novel approach based on artificial intelligence (AI) to assess the efficiency of wave energy converter (WEC) farms in coastal protection is developed. We consider as a case study a beach subjected to severe erosion: Playa Granada (S Spain). More specifically, we analyse the changes in the dry beach area (quantified through the Pelnard-Considère equation) with and without wave farm protection by means of an Artificial Neural Network (ANN) model. The model is selected after a thorough comparative study involving forty ANN architectures, with one and two hidden layers, and two training algorithms (Levenberg-Marquadt and Bayesian regression). The best results are obtained with a [5-10-1] architecture trained with the Bayesian regression algorithm. Once validated, this ANN model is applied to optimize the design and position of the wave farm. The results confirm that ANN models are a useful design tool for hybrid wave farms.
An artificial neural network model of coastal erosion mitigation through wave farms
Title: An artificial neural network model of coastal erosion mitigation through wave farms
September 01, 2019
Journal: Environmental Modelling & Software
Rodriguez-Delgado, C.; Bergillos, R.; Iglesias, G. (2019). An artificial neural network model of coastal erosion mitigation through wave farms. Environmental Modelling & Software, 119, 390-399.