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

Chongxuan Xu; Ying Chen; Xueliang Zhao; Wenyang Song; Xiao Li

doi:10.1007/s13131-023-2149-y

Volume 42 Issue 10

Oct. 2023

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Article Navigation > Acta Oceanologica Sinica > 2023 > 42(10): 97-107

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

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

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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

1.
Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2.
Center for Hydrogeology and Environmental Geology, China Geological Survey, Geological Environment Monitoring Engineering Technology Innovation Center of the Ministry of Natural Resources, Baoding 071051, China

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

Abstract

Abstract

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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References(44)

References

Alizadeh M J, Kavianpour M R. 2015. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Marine Pollution Bulletin, 98(1–2): 171–178

Bandt C, Pompe B. 2002. Permutation entropy: A natural complexity measure for time series. Physical Review Letters, 88(17): 174102. doi: 10.1103/PhysRevLett.88.174102

Cao Liangyue. 1997. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 110(1–2): 43–50

Cerrada M, Sánchez R V, Li Chuan, et al. 2018. A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 99: 169–196. doi: 10.1016/j.ymssp.2017.06.012

Chaudhuri T, Wu Min, Zhang Yu, et al. 2021. An attention-based deep sequential GRU model for sensor drift compensation. IEEE Sensors Journal, 21(6): 7908–7917. doi: 10.1109/JSEN.2020.3044388

Chen Guangyong, Chen Pengfei, Shi Yujun, et al. 2019. Rethinking the usage of batch normalization and dropout in the training of deep neural networks. arXiv preprint arXiv: 1905.05928. https://arxiv.org/abs/1905.05928[2019-05-14/2023-03-27]

Chen Ying, Xu Chongxuan, Zhao Xueliang. 2023. Research on soft compensation of the potential drift signal of a pH electrode based on a gated recurrent neural network. Measurement Science and Technology, 34(2): 025107. doi: 10.1088/1361-6501/ac9ad2

Cho K, Van Merriënboer B, Gulcehre C, et al. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv: 1406.1078. https://arxiv.org/abs/1406.1078[2014-06-03/2022-05-18].

Dai Sicheng, Liu Yiru, Meng Jun. 2021. Sunspot forecast using Temporal Convolutional Neural (TCN) network based on phase space reconstruction. In: 2021 33rd Chinese Control and Decision Conference (CCDC). Kunming, China: IEEE, 2895–2900

Du Liuqing, Li Baochuan, Guo Jiuhao, et al. 2021. Prediction of machine tool’s accuracy degradation based on chaotic phase space reconstruction and depth GRU. In: 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI). Kunming, China: IEEE, 157–161

Duan W Y, Han Y, Huang L M, et al. 2016. A hybrid EMD-SVR model for the short-term prediction of significant wave height. Ocean Engineering, 124: 54–73. doi: 10.1016/j.oceaneng.2016.05.049

Fraser A M, Swinney H L. 1986. Independent coordinates for strange attractors from mutual information. Physical Review A, 33(2): 1134–1140. doi: 10.1103/PhysRevA.33.1134

Gao Song, Huang Juan, Li Yaru, et al. 2021. A forecasting model for wave heights based on a long short-term memory neural network. Acta Oceanologica Sinica, 40(1): 62–69. doi: 10.1007/s13131-020-1680-3

Halevy I, Bachan A. 2017. The geologic history of seawater pH. Science, 355(6329): 1069–1071. doi: 10.1126/science.aal4151

Hu Likun, Su Hao, Cui Ruyao, et al. 2022. KPI anomaly detection based on LSTM with phase space. In: 2022 IEEE 5th International Conference on Big Data and Artificial Intelligence (BDAI). Fuzhou, China: IEEE, 130–135

Jiang Yuchen, Yin Shen, Dong Jingwei, et al. 2021. A review on soft sensors for monitoring, control, and optimization of industrial processes. IEEE Sensors Journal, 21(11): 12868–12881. doi: 10.1109/JSEN.2020.3033153

Kajiyama T, D’Alimonte D, Cunha J C. 2011. Performance prediction of ocean color Monte Carlo simulations using multi-layer perceptron neural networks. Procedia Computer Science, 4: 2186–2195. doi: 10.1016/j.procs.2011.04.239

Keshan N, Parimi P V, Bichindaritz I. 2015. Machine learning for stress detection from ECG signals in automobile drivers. In: 2015 IEEE International Conference on Big Data (Big Data). Santa Clara, CA, USA: IEEE, 2661–2669

Krzysztofowicz R. 2001. The case for probabilistic forecasting in hydrology. Journal of Hydrology, 249(1–4): 2–9

Lee T L. 2004. Back-propagation neural network for long-term tidal predictions. Ocean Engineering, 31(2): 225–238. doi: 10.1016/S0029-8018(03)00115-X

Li Xinfang, Cao Jinfeng, Guo Jihong, et al. 2022. Multi-step forecasting of ocean wave height using gate recurrent unit networks with multivariate time series. Ocean Engineering, 248: 110689. doi: 10.1016/j.oceaneng.2022.110689

Liu Fagui, Zheng Jingzhong, Zheng Lailei, et al. 2020. Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification. Neurocomputing, 371: 39–50. doi: 10.1016/j.neucom.2019.09.012

Malik A, Kumar A, Singh R P. 2019. Application of heuristic approaches for prediction of hydrological drought using multi-scalar streamflow drought index. Water Resources Management, 33(11): 3985–4006. doi: 10.1007/s11269-019-02350-4

Moscoso-Lopez J A, Ruiz-Aguilar J J, Gonzalez-Enrique J, et al. 2019. Ro-Ro freight prediction using a hybrid approach based on empirical mode decomposition, permutation entropy and artificial neural networks. In: 14th International Conference on Hybrid Artificial Intelligence Systems. León, Spain: Springer, 563–574

Niu Mingfei, Gan Kai, Sun Shaolong, et al. 2017. Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM_2.5 concentration forecasting. Journal of Environmental Management, 196: 110–118

Packard N H, Crutchfield J P, Farmer J D, et al. 1980. Geometry from a time series. Physical Review Letters, 45(9): 712–716. doi: 10.1103/PhysRevLett.45.712

Patil K, Deo M C. 2017. Prediction of daily sea surface temperature using efficient neural networks. Ocean Dynamics, 67(3–4): 357–368

Peng Yanni, Xiang Wanli. 2020. Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction. Physica A: Statistical Mechanics and its Applications, 549: 123913. doi: 10.1016/j.physa.2019.123913

Sadeghifar T, Lama G F C, Sihag P, et al. 2022. Wave height predictions in complex sea flows through soft-computing models: Case study of Persian Gulf. Ocean Engineering, 245: 110467. doi: 10.1016/j.oceaneng.2021.110467

Shan Kun, Ouyang Tian, Wang Xiaoxiao, et al. 2022. Temporal prediction of algal parameters in Three Gorges Reservoir based on highly time-resolved monitoring and long short-term memory network. Journal of Hydrology, 605: 127304. doi: 10.1016/j.jhydrol.2021.127304

Singla P, Duhan M, Saroha S. 2022a. An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network. Earth Science Informatics, 15(1): 291–306. doi: 10.1007/s12145-021-00723-1

Singla P, Duhan M, Saroha S. 2022b. A dual decomposition with error correction strategy based improved hybrid deep learning model to forecast solar irradiance. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(1): 1583–1607

Sun Wei, Wang Yuwei. 2018. Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network. Energy Conversion and Management, 157: 1–12. doi: 10.1016/j.enconman.2017.11.067

Takens F. 1981. Detecting strange attractors in turbulence. In: Dynamical Systems and Turbulence, Warwick 1980. Berlin, Heidelberg: Springer, 366–381

Tilbrook B, Jewett E B, DeGrandpre M D, et al. 2019. An enhanced ocean acidification observing network: from people to technology to data synthesis and information exchange. Frontiers in Marine Science, 6: 337. doi: 10.3389/fmars.2019.00337

Vaswani A, Shazeer N, Parmar N, et al. 2017. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA: Curran Associates Inc., 6000–6010

Wang Jianjin, Shi Peng, Jiang Peng, et al. 2017. Application of BP neural network algorithm in traditional hydrological model for flood forecasting. Water, 9(1): 48. doi: 10.3390/w9010048

Wu Zhiyuan, Jiang Changbo, Conde M, et al. 2019. Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature. Ocean Science, 15(2): 349–360. doi: 10.5194/os-15-349-2019

Xie Jiang, Ouyang Jiaming, Zhang Jiyuan, et al. 2022. An evolving sea surface temperature predicting method based on multidimensional spatiotemporal influences. IEEE Geoscience and Remote Sensing Letters, 19: 1502005

Xu Jianlong, Wang Kun, Lin Che, et al. 2021. FM-GRU: A time series prediction method for water quality based on seq2seq framework. Water, 13(8): 1031. doi: 10.3390/w13081031

Yang Jing, Reichert P, Abbaspour K C, et al. 2007. Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference. Journal of Hydrology, 340(3–4): 167–182

Yaseen Z M. 2021. An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere, 277: 130126. doi: 10.1016/j.chemosphere.2021.130126

Zhang Zichen, Ding Shifei, Sun Yuting. 2020. A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task. Neurocomputing, 410: 185–201. doi: 10.1016/j.neucom.2020.05.075

Zhao Xinguo, Han Yu, Chen Bijuan, et al. 2020. CO₂-driven ocean acidification weakens mussel shell defense capacity and induces global molecular compensatory responses. Chemosphere, 243: 125415. doi: 10.1016/j.chemosphere.2019.125415

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