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Characteristics and Prediction of Reservoir Water Quality under the Rainfall-Runoff Impact by Long Short-Term Memory Based Encoder-Decoder Model

Author

Listed:
  • Xiaodan Sheng

    (Shenyang Jianzhu University
    Liaoning Dahuofang Reservoir Administration Co., Ltd)

  • Yulan Tang

    (Shenyang Jianzhu University)

  • Shupeng Yue

    (Liaoning Water Conservancy and Hydropower Survey and Design Research Institute Co., Ltd)

  • Xu Yang

    (Liaoning Dahuofang Reservoir Administration Co., Ltd)

  • Yating He

    (Shenyang Jianzhu University)

Abstract

In the context of global and regional hydrometeorological environmental changes, extreme weather events have become increasingly common, resulting in heavy precipitation and flooding. This has led to an increased risk of rainfall-runoff pollution. This study investigated the evolution of water quality in Dahuofang Reservoir (DHFR) under the influence of rainfall-runoff, utilising water quality monitoring data from the DHFR in Northeast China. Furthermore, an encoder-decoder model based on Long Short-Term Memory (LSTM-ED) was constructed to predict the water quality indicators, and the response of these indicators to different levels of rainfall-runoff was subsequently explored. The findings demonstrate that elevated precipitation levels led to an increase in pH and the concentrations of the permanganate index (CODMn) and total phosphorus (TP) in the DHFR, while the concentrations of dissolved oxygen (DO) and total nitrogen (TN) exhibited a decline. The LSTM-ED model constructed in this study was effective in predicting the changes in water quality indicators in DHFR. It was observed that as the characteristic variables affecting the water quality indicators increased gradually, the concentration of each water quality indicator exhibited an increasing trend. Notably, the amplitude of the pH increase gradually diminished, while the amplitude of the TN and DO increases gradually increased. In particular, our findings indicated that the concentrations of TN and CODMn will reach the standards for inferior Class V and Class III-IV surface water, respectively, when the characteristic variables affecting TN and CODMn concentrations reached their historical maximum levels.

Suggested Citation

  • Xiaodan Sheng & Yulan Tang & Shupeng Yue & Xu Yang & Yating He, 2025. "Characteristics and Prediction of Reservoir Water Quality under the Rainfall-Runoff Impact by Long Short-Term Memory Based Encoder-Decoder Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 1281-1299, February.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:3:d:10.1007_s11269-024-04033-1
    DOI: 10.1007/s11269-024-04033-1
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    References listed on IDEAS

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    1. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).
    2. Shahin Zandmoghaddam & Ali Nazemi & Elmira Hassanzadeh & Shadi Hatami, 2019. "Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3579-3594, August.
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