Integration of data-driven and physics-based modeling of wind waves in a shallow estuary

Abstract

Numerical models solving the wave action balance equation have been widely used to simulate wind waves. In-situ measurements, albeit sparse, are crucial to the calibration and validation of numerical wave models. In this study, a novel hybrid approach was developed by integrating a physics-based Simulating WAves Nearshore (SWAN) model with machine learning algorithms to predict wind waves in a shallow estuary. Two machine learning methods, bagged regression tree (BRT) and artificial neural network (ANN), were employed. It was found that the hybrid approach (BRT–SWAN) could be an efficient tool for modelers to identify sources of error and calibrate parameters in physics-based models. In this study, the wind direction and bottom friction coefficient were determined as the main factors causing errors in SWAN-simulated significant wave height and peak wave period, respectively. Furthermore, it turned out that BRT–SWAN-ANN (ANN trained with BRT–SWAN results) could achieve a similar level of accuracy to OBS-ANN (ANN trained with field observations of wind waves). Thus, the hybrid approach can be applied to estimate wave parameters, removing the limitation of using scarce observations in developing a predictive ANN model.