Obtaining and qualitative analysis of time-lagged correlations between seawater quality parameters

In the regulation of seawater quality, it is crucial to understand the interactions between parameters and the time-lagged effects. This paper focuses on the problem of how to obtain and analyze time-lagged correlations between seawater quality parameters, an issue that has not attracted the attention of researchers. In this paper, a graph neural network-based model, dynamic adjacency weight network, is proposed to model the time-lagged correlation between seawater quality parameters. By regarding the parameters as nodes, the proposed model retains the relationships between the parameters in a weighted adjacency matrix, overcoming the problem of traditional deep neural networks that are difficult to be visualized. Meanwhile, the proposed multivariate multistep shift prediction strategy enables the proposed model to accurately obtain the time-lagged correlation information between parameters at different time intervals. In addition, the proposed model also addresses some of the details worth considering when obtaining correlations between seawater quality parameters. The proposed model performs well in the prediction of water quality parameters. This indirectly verifies the validity of the obtained correlations and overcomes the difficulty of verifying the validity of statistical methods. This study provides new ideas and methods for seawater quality monitoring and research.