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A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit

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Abstract

Dissolved oxygen is one of the important comprehensive indicators of river water quality, which reflects the degree of pollution in the water body. Monitoring and predicting dissolved oxygen are an important tool for water quality management, which helps to effectively maintain water ecological balance and prevent environmental problems. A single model cannot describe the dynamic characteristics of dissolved oxygen sequence, which affects the prediction accuracy. In order to obtain more accurate dissolved oxygen prediction results, decomposition techniques are commonly used to extract the main fluctuations and trends of water quality sequences. However, the high-frequency modes obtained from decomposition are still unstable. To solve this problem, this paper proposed a hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit. Firstly, dissolved oxygen sequence is preliminarily decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and obtain several intrinsic mode functions (IMF). The fuzzy entropy (FE) is calculated to quantify the complexity of the IMF. Then, variational mode decomposition improved by northern goshawk optimization is used to decompose the IMF with higher entropy. The nonlinearity and instability of the sequence are further weakened. Finally, the bidirectional gate recurrent unit (BiGRU) neural network is used to predict each IMF component, and the final prediction result is obtained by reconstructing the prediction results of each component. In order to verify the effectiveness of the proposed model, this paper selects the dissolved oxygen data of Xin'anjiang Reservoir as the research object. The experimental results show that the RMSE, MAE, MAPE, and R2 of the proposed model are 0.1164, 0.0894, 1.0403%, and 0.9939, respectively, which is best among other comparative prediction models (BP, LSTM, GRU, BiGRU, EMD-BiGRU, CEEMDAN-BiGRU, VMD-BiGRU, and GNO-VMD-BiGRU). Therefore, this model effectively deals with high volatility and nonlinear dissolved oxygen data and provides reference for water environment management and ecological protection.

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Data availability

The data used in this paper were obtained from China Environmental Monitoring Station. URL: http://www.cnemc.cn/

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Funding

This study is founded by Natural Science Foundation of Zhejiang Province (LQ20E090006), the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China under Grant No (LZJWY24E090001) and the National Key Research and Development Program of China (No. 2023YFC3207500).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, 310018, China

    Jiange Jiao, Qianqian Ma, Fanglin Liu & Liqin Zhao

  2. Powerchina Huadong Engineering Corporation Limited, Hangzhou, 310014, China

    Senjun Huang

  3. Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, Hangzhou, China

    Jiange Jiao

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  1. Jiange Jiao
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Contributions

All authors contributed to the study conception and design. Methods, data collection and analysis were performed by QM, JJ, LZ, and FL. The first draft of the manuscript was written by QM. SH and JJ were mainly responsible for proofreading the manuscript. All authors read and approved the final manuscript.

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Correspondence to Senjun Huang.

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Jiao, J., Ma, Q., Liu, F. et al. A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit. Environ Geochem Health 46, 127 (2024). https://doi.org/10.1007/s10653-024-01884-w

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