Abstract
In order to obtain forward states of coastal currents, numerical models are a commonly used approach. However, the accurate definition of initial conditions, boundary conditions and other model parameters are challenging. In this paper, a novel application of a soft computing approach, random forests (RF), was adopted to estimate surface currents for three analysis points in Galway Bay, Ireland. Outputs from a numerical model and observations from a high frequency radar system were used as inputs to develop soft computing models. The input variable structure of soft computing models was examined in detail through sensitivity experiments. High correlation of surface currents between predictions from RF models and radar data indicated that the RF algorithm is a most promising means of generating satisfactory surface currents over a long prediction period. Furthermore, training dataset lengths were examined to investigate influences on prediction accuracy. The largest improvement for zonal and meridional surface velocity components over a 59-h forecasting period was 14% and 37% of root mean square error (RMSE) values separately. Results indicate that the combination of RF models with a numerical model can significantly improve forecasting accuracy for surface currents, especially for the meridional surface velocity component.
Highlights
Interaction between atmospheric forces such as wind, river inflows and tide drive the movement of coastal water bodies
random forests (RF) models were established for predicting surface currents based on training the relationship between two datasets
Outputs of surface current components from a coastal hydrodynamic environment fluid dynamics code (EFDC) model, along with tidal elevations, wind speeds and directions were taken as input variables to establish RF models for components of surface water velocity
Summary
Interaction between atmospheric forces such as wind, river inflows and tide drive the movement of coastal water bodies. The horizontal phenomenon related to this movement is surface current. Numerical models and observation platforms are powerful and conventional tools to study patterns of coastal surface currents, providing useful information [1]. Model prediction accuracy over the long term is hard to guarantee. Oceanic observation tools such as satellites and radars based on electro communication technologies are useful means to record surface flow information over a large coastal domain [2].
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