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Prediction of seawater dissolved oxygen concentration based on multi-buoy spatial multi-feature fusion
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Qiguang Zhu1, Zhen Shen1, Xiang Li1, Zhen Wei2, Wenjing Qiao1, Linsong Zhang1, Ying Chen2, *
Haiyang Xuebao | 2025, 47(1) : 104 - 116
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Haiyang Xuebao | 2025, 47(1): 104-116
Article
Prediction of seawater dissolved oxygen concentration based on multi-buoy spatial multi-feature fusion
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Qiguang Zhu1, Zhen Shen1, Xiang Li1, Zhen Wei2, Wenjing Qiao1, Linsong Zhang1, Ying Chen2, *
Affiliations
  • 1. Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
  • 2. Key Laboratory of Measurement Technology and Instrument of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
Published: 2025-01-31 doi: 10.12284/hyxb2025028
Outline
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Dissolved oxygen concentration is one of the important indexes to measure seawater quality. In order to grasp the change of seawater quality in time and reduce the risk and loss of seawater pollution, it is very important to establish the prediction mechanism of marine water quality parameters. Therefore, this paper proposes a prediction model of dissolved oxygen concentration in seawater based on temporal and spatial information fusion of buoy Networks and Generative Adversarial Networks (GAN), which aims to integrate topological information of buoy networks in the monitoring area and realize multi-feature fusion of buoy sensors. The model uses the Graph Attention Mechanism (GAT) to mine the influence of different nearest neighbor points on the target node and calculate the weights of the adjacent nodes, so as to capture the spatio-temporal characteristics of the buoy data. The two-head attention mechanism and the two-time-scale Update Rule (TTUR) were used to optimize the GAN prediction network and the network training process, improve the training speed balance of the generated adversarial network, and improve the fitting effect of the generator network. The mean squared error, root mean squared error, mean absolute error and R-Square are used as evaluation indexes to compare the model prediction performance. The results show that the evaluation indexes of the proposed model are superior to other models, and can effectively mine the spatial information of multiple buoys. It overcomes the shortcomings of traditional methods in the prediction of dissolved oxygen concentration in seawater, such as low accuracy, inability to flexibly use historical spatial data, poor training stability and slow speed, and can provide important technical support for marine water quality monitoring and prediction.

prediction of dissolved oxygen concentration  /  spatial multi-feature fusion  /  Graph Attention Mechanism  /  Generative Adversarial Networks  /  Two Time-Scale Update Rule
Qiguang Zhu, Zhen Shen, Xiang Li, Zhen Wei, Wenjing Qiao, Linsong Zhang, Ying Chen. Prediction of seawater dissolved oxygen concentration based on multi-buoy spatial multi-feature fusion[J]. Haiyang Xuebao, 2025 , 47 (1) : 104 -116 . DOI: 10.12284/hyxb2025028
Year 2025 volume 47 Issue 1
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Article Info
doi: 10.12284/hyxb2025028
  • Receive Date:2024-09-28
  • Online Date:2025-11-10
  • Published:2025-01-31
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History
  • Received:2024-09-28
  • Revised:2024-12-18
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Affiliations
    1. Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
    2. Key Laboratory of Measurement Technology and Instrument of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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