Forecasting of surface current velocities using ensemble machine learning algorithms for the Guangdong-Hong Kong-Macao Greater Bay Area based on the High Frequency radar data

Forecasting of ocean currents is critical for both marine meteorological research and ocean engineering and construction. Timely and accurate forecasting of coastal current velocities offers a scientific foundation and decision support for multiple practices such as search and rescue, disaster avoidance and remediation, and offshore construction. This research established a framework to generate short-term surface current forecasts based on ensemble machine learning trained on High Frequency radar observation. Results indicate that an ensemble algorithm that used random forests to filter forecasting features by weighting them, and then used the AdaBoost method to forecast can significantly reduce the model training time, while ensuring the model forecasting effectiveness, with great economic benefits. Model accuracy is a function of surface current variability and the forecasting horizon. In order to improve the forecasting capability and accuracy of the model, the model structure of the ensemble algorithm was optimized, and the random forest algorithm was used to dynamically select model features. The results show that the error variation of the optimized surface current forecasting model has a more regular error variation, and the importance of the features varies with the forecasting time-step. At ten-step ahead forecasting horizon the model reported RMSE, MAE and correlation coefficient by 2.84 cm/s, 2.02 cm/s, and 0.96, respectively. The model error is affected by factors such as topography, boundaries, and geometric accuracy of the observation system. This paper demonstrates the potential of ensemble-based machine learning algorithm to improve forecasting of ocean currents.