Prediction of Surface Currents Using High Frequency CODAR Data and Decision Tree at a Marine Renewable Energy Test Site

In this study, Decision Tree (DT) was employed to predict surface currents in a ¼-scaled marine renewable energy test site-Galway Bay. In training and testing models, wind speed, wind direction and tidal water elevation from a forecasting model, and observations of surface velocity components during previous hours were taken as input variables; surface velocity components were taken as the output variable. Appropriate value of Complexity Parameter (CP) in decision tree models was determined based on experiments producing the minimum Root-Mean-Square-Error (RMSE) values compared with the radar data. Statistics including RMSE, bias, correlation (R) and Scatter Index (SI) were computed between predictions and radar data to assess predictions. Results indicated that the DT model can produce satisfactory predictions of surface currents. Good performance of DT model indicated that it can be regarded as a promising approach for future applications.