A novel approach towards gap filling of high-frequency radar time-series data

Abstract

The real-time monitoring of the coastal and marine environment is vital for various reasons including oil spill detection and maritime security amongst others. Systems such as High Frequency Radar (HFR) networks are able to record sea surface currents in real-time. Unfortunately, such systems can suffer from malfunctions caused by extreme weather conditions or frequency interference, thus leading to a degradation in the monitoring system coverage. This results in sporadic gaps within the observation datasets. To counter this problem, the use of deep learning techniques has been investigated to perform gap-filling of the HFR data. Additional features such as remotely sensed wind data were also considered to try enhance the prediction accuracy of these models. Furthermore, look-back values between 3 and 24 hours were investigated to uncover the minimal amount of historical data required to make accurate predictions. Finally, drift in the data was also analysed, determining how often these model architectures might require re-training to keep them valid for predicting future data