In this paper, the data feature of depth-averaged current velocities (DACVs) derived from underwater gliders is analyzed for the first time. Two features of DACVs have been proposed: one is the complex ingredients and small samples, and the other is the stationarity that occurs as the length of a DACV sequence increases. With these features in mind, a set of methods combining statistical analysis and machine learning are proposed to realize the prediction of DACVs. Four groups of DACV data of different gliders from sea trials in the South China Sea are used to verify the prediction method. Based on three general error criteria, the prediction performance of the proposed model is demonstrated. The persistence method is used as a comparison model. The results show that the prediction methods proposed in this paper are effective.

This work is published on AIP ADVANCES 11.7(2021):1-7.