Volume 42 Issue 10
Oct.  2023
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Chongxuan Xu, Ying Chen, Xueliang Zhao, Wenyang Song, Xiao Li. Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction: case study of the coastal waters of Beihai, China[J]. Acta Oceanologica Sinica, 2023, 42(10): 97-107. doi: 10.1007/s13131-023-2149-y
Citation: Chongxuan Xu, Ying Chen, Xueliang Zhao, Wenyang Song, Xiao Li. Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction: case study of the coastal waters of Beihai, China[J]. Acta Oceanologica Sinica, 2023, 42(10): 97-107. doi: 10.1007/s13131-023-2149-y

Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction: case study of the coastal waters of Beihai, China

doi: 10.1007/s13131-023-2149-y
Funds:  The National Natural Science Foundation of China under contract No. 62275228; the S&T Program of Hebei under contract Nos 19273901D and 20373301D; the Hebei Natural Science Foundation under contract No. F2020203066.
More Information
  • Corresponding author: E-mail: chenying@ysu.edu.cn
  • Received Date: 2022-08-23
  • Accepted Date: 2023-02-02
  • Available Online: 2023-08-07
  • Publish Date: 2023-10-01
  • Marine life is very sensitive to changes in pH. Even slight changes can cause ecosystems to collapse. Therefore, understanding the future pH of seawater is of great significance for the protection of the marine environment. At present, the monitoring method of seawater pH has been matured. However, how to accurately predict future changes has been lacking effective solutions. Based on this, the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction (ICPBGA) is proposed to achieve seawater pH prediction. To verify the validity of this model, pH data of two monitoring sites in the coastal sea area of Beihai, China are selected to verify the effect. At the same time, the ICPBGA model is compared with other excellent models for predicting chaotic time series, and root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2) are used as performance evaluation indicators. The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9, and the prediction errors are also the smallest. The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect. The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.
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