Time Series Prediction Methods for Depth-Averaged Current Velocities of Underwater Gliders

Abstract

In this paper, we propose time series prediction methods for depth-averaged current velocities (DACVs) of underwater gliders. Based on historical DACV data, these methods can predict the DACVs of future profiles with good performance. Regarding DACVs as time series, we use backpropagation neural network and least squares support vector machine (LSSVM) methods to predict the DACVs. To obtain better prediction performance, the features of DACVs are considered, and we use empirical mode decomposition (EMD) to decompose the time series into several sub-series. Then, the two methods are reused to predict each sub-series, and the results of all the sub-series with each method are added. Based on the real-time DACVs obtained from the simulation environment and the DACVs obtained from sea trials, we test and verify the four methods. The results demonstrate that all the methods exhibit a good prediction performance for conditions in which ocean currents are relatively regular; whereas in other cases, EMD-LSSVM shows inherent robustness and superiority compared with the other three methods.