Gated Recurrent Unit Network of Particle Swarm Optimization for Drifting Buoy Trajectory Prediction

LIU SongZuo; WANG Qian; LI Lei; LI Hui; YU Yun

doi:10.11999/JEIT230945

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LIU SongZuo, WANG Qian, LI Lei, LI Hui, YU Yun. Gated Recurrent Unit Network of Particle Swarm Optimization for Drifting Buoy Trajectory Prediction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230945

Citation:

LIU SongZuo, WANG Qian, LI Lei, LI Hui, YU Yun. Gated Recurrent Unit Network of Particle Swarm Optimization for Drifting Buoy Trajectory Prediction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230945

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Gated Recurrent Unit Network of Particle Swarm Optimization for Drifting Buoy Trajectory Prediction

doi: 10.11999/JEIT230945

LIU SongZuo^{1, 2, 3, 4},
WANG Qian^{1, 2, 3},
LI Lei⁵,
LI Hui^{6
,
,},
YU Yun⁷

1.
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin, 150001, China
2.
Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin, 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
4.
Sanya Nanhai Innovation and Development Base of Harbin Engineering University, Sanya, 572024, China
5.
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
6.
College of Intelligent Systems Science and Engineering, Harbin Engineering University Harbin 150001, China
7.
Naval Academy of Armament, Beijing 100161, China

Funds: The National Natural Science Foundation of China (61803115)

Received Date: 2023-08-31
Rev Recd Date: 2024-05-13

Available Online: 2024-05-22

Abstract

Abstract

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.
- Drifting buoy,
- Trajectory prediction,
- Particle Swarm Optimization(PSO),
- Gated Recurrent Unit (GRU)

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References(22)

References

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