In this paper, an implementation study was undertaken to employ Artificial Neural Networks (ANN) in third-generation ocean wave models for direct mapping of wind-wave spectra into exact nonlinear interactions. While the investigation expands on previously reported feasibility studies of Neural Network Interaction Approximations (NNIA), it focuses on a new robust neural network that is implemented in Wavewatch III (WW3) model. Several idealistic and real test scenarios were carried out. The obtained results confirm the feasibility of NNIA in terms of speeding-up model calculations and is fully capable of providing operationally acceptable model integrations. The ANN is able to emulate the exact nonlinear interaction for single- And multimodal wave spectra with a much higher accuracy then Discrete Interaction Approximation (DIA). NNIA performs at least twice as fast as DIA and at least two hundred times faster than exact method (Web-Resio-Tracy, WRT) for a well trained dataset. The accuracy of NNIA is network configuration dependent. For most optimal network configurations, the NNIA results and scatter statistics show good agreement with exact results by means of growth curves and integral parameters. Practical possibilities for further improvements in achieving fast and highly accurate emulations using ANN for emulating time consuming exact nonlinear interactions are also suggested and discussed. © The Authors. Published by Elsevier B.V.
Puscasu, R. M. (2014). Integration of artificial neural networks into operational ocean wave prediction models for fast and accurate emulation of exact nonlinear interactions. In Procedia Computer Science (Vol. 29, pp. 1156–1170). Elsevier. https://doi.org/10.1016/j.procs.2014.05.104