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Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

Author

Listed:
  • Jiafeng Pang

    (Sun Yat-Sen University)

  • Wei Luo

    (Sun Yat-Sen University)

  • Zeyu Yao

    (Sun Yat-Sen University)

  • Jing Chen

    (Guangzhou Feng Ze-Yuan Water Conservancy Technology Co., Ltd)

  • Chunyu Dong

    (Sun Yat-Sen University
    Southern Marine Science and Engineering Guangdong Laboratory)

  • Kairong Lin

    (Sun Yat-Sen University
    Southern Marine Science and Engineering Guangdong Laboratory)

Abstract

The prediction of water quality in urban rivers plays a crucial role in supporting water environment management. This study collected real-time water quality monitoring data from four stations in the Fenjiang River Basin of Foshan City, spanning from 2016 to 2021. Then the Wavelet Packet Denoising (WPD) technique was applied to reduce noise interference in historical monitoring data. Subsequently, a single-factor water quality prediction model was developed, which is based on Long Short-Term Memory (LSTM), focusing on chemical oxygen demand (COD) and ammonia nitrogen (NH3-N). The results of this study demonstrate that the integration of WPD with LSTM, referred to as WPD-LSTM, outperformed conventional LSTM models in terms of predictive accuracy. Notably, the WPD-LSTM model exhibited superior performance in predicting the impact of COD and NH3-N on water quality in the Fenjiang River, surpassing the traditional LSTM model over a prediction period of 12 h and 3 days. In the 12-h prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 55% to 67%, and the RMSE values of COD predictions decreased by 18% to 51%.. In the 3-day prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 40% to 83%, and the RMSE values of COD predictions decreased by 50% to 69%. By employing the WPD-LSTM method, this study contributes to improving the precision of water quality prediction, thereby providing valuable insights for effective water environment management in urban river systems.

Suggested Citation

  • Jiafeng Pang & Wei Luo & Zeyu Yao & Jing Chen & Chunyu Dong & Kairong Lin, 2024. "Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(7), pages 2399-2420, May.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:7:d:10.1007_s11269-024-03774-3
    DOI: 10.1007/s11269-024-03774-3
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    References listed on IDEAS

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    1. D. Nagesh Kumar & K. Srinivasa Raju & T. Sathish, 2004. "River Flow Forecasting using Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 143-161, April.
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    4. Ping Liu & Jin Wang & Arun Kumar Sangaiah & Yang Xie & Xinchun Yin, 2019. "Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
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    1. Bhagwan Das & Amr Adel & Tony Jan & M. D. Wahiduzzaman, 2025. "Water Quality Management using Federated Deep Learning in Developing Southeastern Asian Country," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(4), pages 1893-1909, March.

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