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Research on Water Level Prediction of Downstream Giant Hydropower Plant Under Backwater Effect
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Wang PENG1, 2, Hua-ming YAO2, Zhi-qiang JIANG1, Hui CAO2
Water Resources and Power | 2025, 43(9) : 203 - 207
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Water Resources and Power | 2025, 43(9): 203-207
Research on Water Level Prediction of Downstream Giant Hydropower Plant Under Backwater Effect
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Wang PENG1, 2, Hua-ming YAO2, Zhi-qiang JIANG1, Hui CAO2
Affiliations
  • 1.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 2.China Yangtze Power Co., Ltd., Yichang 443002, China
Published: 2025-09-25 doi: 10.20040/j.cnki.1000-7709.2025.20241784
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The tailwater level of hydropower station is a critical parameter for calculating the unit's output. When influenced by the downstream reservoir's backwater effect, discrepancies often arise between the designed tailwater curve and the actual observed values, leading to increased errors in the output-flow calculations. Utilizing the latest historical observation data, this study explores the relationship between the tailwater level of BHT Hydropower Station, its discharge, and the water level of the downstream XLD Reservoir. A Bayesian optimized long short-term memory (BO-LSTM) prediction model is developed based on multi-scenario analysis. The applied effect is analyzed under conditions of peak load and flood discharge. The results indicate that when the water level of XLD exceeds 585 meters, the tailwater level of BHT Hydropower Station is significantly influenced. Compared to the nonlinear curve fitting method, the BO-LSTM model based multi-scenario analysis demonstrates a substantial improvement in accuracy, with an average absolute error (MMAE) reduced by 68.1%. The BO-LSTM model more accurately captures the fluctuations and changes in water levels under various operating conditions. The research results have important significant for refined operation of hydropower stations.

water-level discharge relationship  /  backwater effect  /  long short-term memory neural networks  /  prediction
Wang PENG, Hua-ming YAO, Zhi-qiang JIANG, Hui CAO. Research on Water Level Prediction of Downstream Giant Hydropower Plant Under Backwater Effect[J]. Water Resources and Power, 2025 , 43 (9) : 203 -207 . DOI: 10.20040/j.cnki.1000-7709.2025.20241784
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Year 2025 volume 43 Issue 9
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Article Info
doi: 10.20040/j.cnki.1000-7709.2025.20241784
  • Receive Date：2024-09-19
  • Online Date：2025-12-15
  • Published：2025-09-25
Article Data
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History
  • Received：2024-09-19
  • Revised：2024-11-20
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Affiliations
    1.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    2.China Yangtze Power Co., Ltd., Yichang 443002, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2025.20241784
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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