Gated Recurrent Unit Network of Particle Swarm Optimization for Drifting Buoy Trajectory Prediction
Considering the trajectory prediction problem of drift buoys,an end-to-end prediction model based on the depth learning framework is proposed in this paper.The hydrodynamic models in different sea areas are quite different,and the calculation of fluid load of floating buoys on the sea surface is also complicated.Therefore,a more universal data-driven trajectory prediction model based on the multidimensional time series formed by the historical trajectories of drifting buoys is proposed.In this model,Particle Swarm Optimization(PSO)is combined with Gated Recurrent Unit(GRU),and the PSO is used to initialize the hyperparameters of the GRU neural network.The optimal drifting buoy trajectory prediction model is obtained after multiple migration iteration training.Finally,several real drifting buoy track data in the North Atlantic are used to verify the results.The results show that the PSOGRU algorithm can achieve accurate drifting buoy track prediction results.