Pattern Recognition for Imputation of Missing Radial Surface Current Data

Surface currents can be accurately measured remotely using high-frequency radars, with the drawback that those measurements are susceptible to external interference resulting in frequent gaps in data. In this article, we compare the gap-filling accuracy of four pattern recognition machine learning methods—k-means clustering, self-organizing maps, growing neural gas, and a generative adversarial network. Several dozen experiments are demonstrated using data from two different radars, exploring the possibilities of applications of feature engineering to reduce the dimensionality of the problem. Findings indicate how classical pattern recognition algorithms result in an average relative error of around 5%–10<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, while the generative adversarial network decreased that error, with significantly increased correlation.