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Prediction Study of Pollutants in Artificial Wetlands Enhanced by Electromagnetic Fields

Author

Listed:
  • Fajin Yin

    (School of Mechanical Engineering and Transportation, Southwest Forestry University, Kunming 650233, China
    Key Laboratory of Ecological Environment Evolution and Pollution Control in Mountainous and Rural Areas of Yunnan Province, Kunming 650224, China)

  • Rong Ma

    (School of Mechanical Engineering and Transportation, Southwest Forestry University, Kunming 650233, China
    Key Laboratory of Ecological Environment Evolution and Pollution Control in Mountainous and Rural Areas of Yunnan Province, Kunming 650224, China)

  • Yungen Liu

    (Key Laboratory of Ecological Environment Evolution and Pollution Control in Mountainous and Rural Areas of Yunnan Province, Kunming 650224, China)

  • Liechao Xiong

    (School of Mechanical Engineering and Transportation, Southwest Forestry University, Kunming 650233, China
    Key Laboratory of Ecological Environment Evolution and Pollution Control in Mountainous and Rural Areas of Yunnan Province, Kunming 650224, China)

  • Hu Luo

    (School of Mechanical Engineering and Transportation, Southwest Forestry University, Kunming 650233, China
    Key Laboratory of Ecological Environment Evolution and Pollution Control in Mountainous and Rural Areas of Yunnan Province, Kunming 650224, China)

Abstract

Predictive modelling is very important for water pollution management. In this study, based on an electromagnetic field-enhanced vertical flow artificial wetland and using the actual measured data as inputs to the model, the ammonia nitrogen (NH 4 + -N) effluent concentration of this wetland system was analyzed by Pearson’s correlation analysis to be related to six key factors, which were the NH + -N raw water concentration, the chemical oxygen demand (COD) raw water concentration, the treatment time, the magnetic field strength, the aeration time, and the electric field strength. Then, different artificial neural network models were constructed for comparison and the constructed models were evaluated based on statistical parameters. The results show that the PSO algorithm can improve the prediction effect of the BP neural network, but the prediction accuracy of the CNN model is better compared to the others. The prediction accuracy of the RF model is the highest compared to the others, and the evaluation parameters of R2, RMSE, and MAE of the test set are (0.9446, 2.4328, and 3.0943), respectively. The prediction error of this model is the smallest, and the model can predict the concentration of electric and magnetic fields in a wetland system with high accuracy compared to other models. This model can more accurately predict the NH 4 + -N effluent concentration of the magnetic field-enhanced wetland system, which can provide a certain basis for the study of the management of water pollution.

Suggested Citation

  • Fajin Yin & Rong Ma & Yungen Liu & Liechao Xiong & Hu Luo, 2024. "Prediction Study of Pollutants in Artificial Wetlands Enhanced by Electromagnetic Fields," Sustainability, MDPI, vol. 16(23), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10327-:d:1529515
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    References listed on IDEAS

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    1. Feng, Yu & Cui, Ningbo & Gong, Daozhi & Zhang, Qingwen & Zhao, Lu, 2017. "Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling," Agricultural Water Management, Elsevier, vol. 193(C), pages 163-173.
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