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Short-Term Water Level Prediction for Long-Distance Water Diversion Projects Using Data-Driven Methods with Multi-Scale Attention Mechanism

Author

Listed:
  • Xinyong Xu

    (North China University of Water Resources and Electric Power
    Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering)

  • Zixuan Zhu

    (North China University of Water Resources and Electric Power)

  • Xiaonan Chen

    (China South-to-North Water Diversion Middle Route Corporation Limited)

  • Bo Wang

    (North China University of Water Resources and Electric Power)

  • Xianliang Liu

    (North China University of Water Resources and Electric Power
    China South-to-North Water Diversion Middle Route Corporation Limited
    Nanyang Normal University)

  • Li Jiang

    (North China University of Water Resources and Electric Power)

  • Zhenbao Wang

    (North China University of Water Resources and Electric Power)

  • Yuxian Qiu

    (North China University of Water Resources and Electric Power)

  • Laike Yang

    (North China University of Water Resources and Electric Power)

  • Xu Qi

    (North China University of Water Resources and Electric Power)

  • Hao Lu

    (North China University of Water Resources and Electric Power)

Abstract

Short-term water level prediction plays a crucial role in the control of gates for long-distance water diversion projects, enabling decision makers to make informed gate control decisions and ensure the safe and stable operation of water resources allocation projects. In this study, a short-term water level prediction coupling model for long-distance water diversion projects, It comprises three components: the Wavelet threshold denoising (WTD) method, Reversible Instance Normalization (RevIN), and Crossformer. The model acquires data that is more conducive to the model’s mining of the water level change law through low-frequency information extraction and data smoothing. Through multi-scale learning, time dimension and feature dimension correlation synchronization analysis, the full excavation of water level change law is realized. The generalization ability and robustness of the proposed method are verified by ablation test, comparison test with benchmark model, application of different forecast periods and different control gates. The results indicate that the coupling of WTD, RevIN, and Crossformer can improve the accuracy of short-term water level prediction, and the data smoothing method utilizing RevIN is more effective in improving the model. Compared with LSTM and Transformer, the accuracy of the proposed method is significantly improved, with a maximum increase of 82.25%, which is more in line with the short-term (6 ~ 24h) water level prediction of long-distance water diversion projects. This study can provide theoretical and technical support for the safe dispatching of long-distance water diversion projects, and provide useful reference for time series prediction of other similar water conservancy projects.

Suggested Citation

  • Xinyong Xu & Zixuan Zhu & Xiaonan Chen & Bo Wang & Xianliang Liu & Li Jiang & Zhenbao Wang & Yuxian Qiu & Laike Yang & Xu Qi & Hao Lu, 2025. "Short-Term Water Level Prediction for Long-Distance Water Diversion Projects Using Data-Driven Methods with Multi-Scale Attention Mechanism," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(6), pages 2919-2941, April.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:6:d:10.1007_s11269-025-04098-6
    DOI: 10.1007/s11269-025-04098-6
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    References listed on IDEAS

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    1. Abdus Samad Azad & Rajalingam Sokkalingam & Hanita Daud & Sajal Kumar Adhikary & Hifsa Khurshid & Siti Nur Athirah Mazlan & Muhammad Babar Ali Rabbani, 2022. "Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study," Sustainability, MDPI, vol. 14(3), pages 1-20, February.
    2. Xiangyu Sun & Lina Zhang & Chao Wang & Yiyang Yang & Hao Wang, 2024. "Dynamic Real-Time Prediction of Reclaimed Water Volumes Using the Improved Transformer Model and Decomposition Integration Technology," Sustainability, MDPI, vol. 16(15), pages 1-26, August.
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