Data-driven and physics-based approach for wave downscaling: A comparative study

Abstract

The physics-based approach is the most common framework for modelling ocean engineering processes; however, in the last years data-driven models are becoming more and more popular. In the case of wave downscaling, the number of studies involving data-driven models is scarce. In this paper, both approaches are developed and compared on a case study in the Mediterranean Sea. Two Feed Forward Multilayer Perceptron Neural Networks are trained with the Levenberg-Marquardt algorithm and compared with a SWAN model. The behaviour of both models turns out to be similar with respect to the wave height. However, with respect to the peak period, neural networks outperform the SWAN model, which slightly overestimates the peak period. Neural networks show a correlation coefficient and error of 0.96 and 1.00 s, respectively, versus 0.72 and 6.2 s for the SWAN model. Regarding the computational cost, the data-driven approach clearly outperforms the physics-based approach. Therefore, it may be concluded that at those sites where a minimum of 25,000 data samples are available, data-driven models are of interest – not least in semi-enclosed basins governed by local patterns, where large-scale or global physics-based models typically struggle.