Fused Spatial–Temporal Graph Convolutional Networks for Ocean Currents Forecasting Using Underwater Glider Measurements

Hourly forecasting of ocean currents is crucial for planning and control of underwater gliders and other small, underactuated underwater robots. It is a common practice to use the velocity of the depth-averaged current for estimating the ocean current surrounding the underwater robot. However, this method does not consider the spatial–temporal correlations of ocean currents, which can result in large errors. In this paper, we propose a novel deep learning fusion model, based on fused spatial–temporal graph convolutional networks (FSTGCN), which incorporates the physical properties and online measurements of ocean currents that can be modeled as Gaussian processes. The correlations between different sampling points of the 2-D ocean current data in 2-D space is constructed exploiting the incompressibility of ocean currents. Then, spatial–temporal graph convolutional networks that fuse the properties of the currents are proposed to extract their spatial–temporal dependencies. Based on measurements of underwater gliders, we further improve the proposed model to fuse model forecasts (weak constraints) and online measurements (strong constraints), which are evaluated using three different datasets and field trial data. The qualitative and quantitative results demonstrate that our fusion model outperforms competing methods and can achieve sufficient prediction accuracy in real ocean scenarios. In brief, the proposed model provides more accurate perception of ocean currents and helps underwater gliders achieve more efficient observation of ocean phenomena than other existing methods.