Prediction of seawater dissolved oxygen concentration based on multi-buoy spatial multi-feature fusion

Zhu Qiguang; Shen Zhen; Li Xiang; Wei Zhen; Qiao Wenjing; Zhang Linsong; Chen Ying

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Zhu Qiguang，Shen Zhen，Li Xiang, et al. Prediction of seawater dissolved oxygen concentration based on multi-buoy spatial multi-feature fusion[J]. Haiyang Xuebao，2025, 47(x)：1–13

Citation:

Zhu Qiguang，Shen Zhen，Li Xiang, et al. Prediction of seawater dissolved oxygen concentration based on multi-buoy spatial multi-feature fusion[J]. Haiyang Xuebao，2025, 47(x)：1–13

Citation:

Zhu Qiguang，Shen Zhen，Li Xiang, et al. Prediction of seawater dissolved oxygen concentration based on multi-buoy spatial multi-feature fusion[J]. Haiyang Xuebao，2025, 47(x)：1–13

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Prediction of seawater dissolved oxygen concentration based on multi-buoy spatial multi-feature fusion

1.
Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China

Received Date: 2024-09-28
Rev Recd Date: 2024-12-18

Available Online: 2025-01-08

Abstract

Abstract

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 adduction 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.

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References(34)

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