Temporal kNN for short-term ocean current prediction based on HF radar observations

Abstract

Ocean surface current prediction is at the core of various marine operational routines, including disaster monitoring, oil-spill backtracking, sea navigation and search-and-rescue operations. More accurate prediction can yield significant improvement to the overall system. Most existing short-term prediction methods applied numerical models based on physical processes. In this paper, we propose an alternative approach in predicting the surface current by utilizing temporal k-nearest-neighbor technique, which can predict the future surface current up to 24 hours in advance. Our model incorporates several pre-processing methods, e.g. feature extraction and data transformation, in order to capture the seasonal and temporal characteristics of the HF (high frequency) radar observation data. The developed model was implemented, validated and compared with the existing models using the same historical datasets collected from the HF coastal radar stations located along the Gulf of Thailand. Our experimental results indicate that the proposed model can achieve the highest accuracy among all methods, including ARIMA, exponential smoothing, and LSTM; and satisfy the oil-spill backtracking application requirements. In addition, we found that our system requires little to none maintenance and can easily be adapted to other coastal radar locations where the amount of historical HF radar observations is limited.