Data-driven depth-averaged current prediction methods for underwater gliders with sailing parameters

Abstract

The study of depth-averaged currents is of great significance for the application of underwater gliders. In order to solve the problem of low prediction accuracy of the time series-based depth-averaged current prediction method, the factors affecting the prediction of depth-averaged currents are analyzed and a data-driven prediction method for depth-averaged currents of an underwater glider with sailing parameters is proposed in this paper. First, depth-averaged currents of the underwater glider’s historical profile period and navigation parameters of the underwater glider are taken as inputs to construct multi-input and double-output characteristics. Then, based on the two sets of the real sea trial data and two groups of the generic set of evaluation criteria, five different data-driven methods are used to predict depth-averaged currents. Experimental results show that the prediction result of depth-averaged currents of an underwater glider driven by data with sailing parameters is better than that based on time series, and the prediction accuracy of depth-averaged currents of a future profile period is improved.