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Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy

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  • Yuqi Dong
  • Jianzhou Wang
  • Xinsong Niu
  • Bo Zeng

Abstract

Water quality forecasting has great practical significance for sustainable utilization of water resources and timely pollution prevention and control. However, owing to irregularity and volatility of water quality data, achieving accurate forecasts remains a challenging problem. Existing single forecasting models based on points forecasts fail to capture the uncertainty in water quality data or obtain high forecasting accuracy. To address those disadvantages, an uncertain combined water quality forecasting system based on time‐varying filtering empirical mode decomposition strategy and multiobjective immune selection optimization is proposed. Simulations are carried out with actual water quality data obtained during dry and wet periods. Empirical results show that the improved empirical mode decomposition strategy has better data denoising effect. Based on the multiobjective optimization algorithm, not only the Pareto optimal weights of the combined forecasting modules can be obtained but also the distribution function parameters can be effectively optimized. The proposed system can integrate the respective advantages of deep learning and neural network models to improve the forecasting accuracy, and effectively analyze the uncertainty of water quality, and provide technical support for the sustainable development of regional water environment.

Suggested Citation

  • Yuqi Dong & Jianzhou Wang & Xinsong Niu & Bo Zeng, 2023. "Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 260-287, March.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:2:p:260-287
    DOI: 10.1002/for.2905
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    References listed on IDEAS

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    1. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    2. Jiang, Ping & Li, Ranran & Liu, Ningning & Gao, Yuyang, 2020. "A novel composite electricity demand forecasting framework by data processing and optimized support vector machine," Applied Energy, Elsevier, vol. 260(C).
    3. Yang, Shaobo & Deng, Zegui & Li, Xingfei & Zheng, Chongwei & Xi, Lintong & Zhuang, Jucheng & Zhang, Zhenquan & Zhang, Zhiyou, 2021. "A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast," Renewable Energy, Elsevier, vol. 173(C), pages 531-543.
    4. Ankita P. Dadhich & Rohit Goyal & Pran N. Dadhich, 2021. "Assessment and Prediction of Groundwater using Geospatial and ANN Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2879-2893, July.
    5. Jiang, He & Tao, Changqi & Dong, Yao & Xiong, Ren, 2021. "Robust low-rank multiple kernel learning with compound regularization," European Journal of Operational Research, Elsevier, vol. 295(2), pages 634-647.
    6. Stephen Stajkowski & Deepak Kumar & Pijush Samui & Hossein Bonakdari & Bahram Gharabaghi, 2020. "Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction," Sustainability, MDPI, vol. 12(13), pages 1-18, July.
    7. Jiang, He & Luo, Shihua & Dong, Yao, 2021. "Simultaneous feature selection and clustering based on square root optimization," European Journal of Operational Research, Elsevier, vol. 289(1), pages 214-231.
    8. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
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    Cited by:

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