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The prediction model of water level in front of the check gate of the LSTM neural network based on AIW-CLPSO

Author

Listed:
  • Linqing Gao

    (Hebei University of Engineering
    Hebei Key Laboratory of Intelligent Water Conservancy)

  • Dengzhe Ha

    (Tianjin University)

  • Litao Ma

    (Hebei University of Engineering)

  • Jiqiang Chen

    (Hebei University of Engineering)

Abstract

To solve the problem of predicting water level in front of check gate under different time scales, a different time scale prediction model with a long short term memory (LSTM) neural network based on adaptive inertia weight comprehensive learning particle swarm optimization (AIW-CLPSO) is proposed. The AIW and CLPSO are adopted to improve the global optimization ability and convergence velocity of particle swarm optimization in the proposed model. The model was applied to the water level prediction in front of the Chaohu Lake check gate. The example of the water level prediction in front of the Chaohu Lake check gate shows that the proposed model predicts the trend of water level fluctuation better than LSTM with high accuracy of Nash coefficient up to 0.9851 and root mean square error up to 0.0273 m. The optimized algorithm can obtain the optimal parameters of the LSTM neural network, overcome the limitations of the traditional LSTM neural network in parameter selection and inaccurate prediction, and maintain good prediction results in the predicting water level in front of the check gate at different time scales.This study can provide important reference for water level prediction, scheduling warning, water resources scheduling decision and intelligent gate control in long distance water transfer projects.

Suggested Citation

  • Linqing Gao & Dengzhe Ha & Litao Ma & Jiqiang Chen, 2024. "The prediction model of water level in front of the check gate of the LSTM neural network based on AIW-CLPSO," Journal of Combinatorial Optimization, Springer, vol. 47(2), pages 1-17, March.
    • Handle: RePEc:spr:jcomop:v:47:y:2024:i:2:d:10.1007_s10878-023-01101-x
    DOI: 10.1007/s10878-023-01101-x
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    References listed on IDEAS

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    1. Bun Theang Ong & Masao Fukushima, 2015. "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 171-194.
    2. Shuangxin Wang & Guibin Tian & Dingli Yu & Yijiang Lin, 2015. "Dynamic Particle Swarm Optimization with Any Irregular Initial Small-World Topology," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 6(4), pages 1-23, October.
    3. Li, Jiale & Song, Zihao & Wang, Xuefei & Wang, Yanru & Jia, Yaya, 2022. "A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD," Energy, Elsevier, vol. 251(C).
    4. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
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    Cited by:

    1. Issam Rehamnia & Amin Mahdavi-Meymand, 2025. "Advancing Reservoir Water Level Predictions: Evaluating Conventional, Ensemble and Integrated Swarm Machine Learning Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(2), pages 779-794, January.

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