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A Dam Deformation Residual Correction Method for High Arch Dams Using Phase Space Reconstruction and an Optimized Long Short-Term Memory Network

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  • Yantao Zhu

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210024, China
    College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China
    National Dam Safety Research Center, Wuhan 430010, China)

  • Mingxia Xie

    (National Dam Safety Research Center, Wuhan 430010, China
    Changjiang Survey, Planning, Design and Research Co., Ltd., Wuhan 430010, China)

  • Kang Zhang

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210024, China
    College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China)

  • Zhipeng Li

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210024, China
    College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China)

Abstract

Dam safety is an important basic part of national water network security. Building a dam deformation prediction model based on monitoring data is crucial to ensure dam safety. However, traditional statistical regression methods have shortcomings, such as a weak nonlinear fitting ability when constructing dam deformation monitoring and prediction models. The residual part of the statistical regression results usually contains parts that cannot be effectively explained by the linear regression method, that is usually highly variable and noisy. In this study, the phase space reconstruction method is used to smooth the residual term of the statistical regression model to eliminate noise interference. On this basis, an improved long short-term memory (LSTM) neural network is used to learn the nonlinearity contained in the residual term of the linear regression. Considering the impact of parameter selection on model performance, the gray wolf optimization (GWO) algorithm is used to determine the optimal parameters of the model for better performance. A high arch dam is used as a case study, with multiple measuring points used as research objects. The experimental results show that the phase space reconstruction can effectively smooth the high-frequency components in the residual term and remove noise interference. In addition, the GWO algorithm can effectively determine the hyperparameters of the LSTM network, thereby constructing a residual prediction model with high prediction accuracy. The combination of statistical models and deep learning prediction methods can effectively improve the model prediction performance while preserving the model interpretability and transparency.

Suggested Citation

  • Yantao Zhu & Mingxia Xie & Kang Zhang & Zhipeng Li, 2023. "A Dam Deformation Residual Correction Method for High Arch Dams Using Phase Space Reconstruction and an Optimized Long Short-Term Memory Network," Mathematics, MDPI, vol. 11(9), pages 1-20, April.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2010-:d:1131153
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    References listed on IDEAS

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    1. Chongshi Gu & Xiao Fu & Chenfei Shao & Zhongwen Shi & Huaizhi Su, 2020. "Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study," IJERPH, MDPI, vol. 17(1), pages 1-25, January.
    2. Zhang, Yuquan & Zang, Wei & Zheng, Jinhai & Cappietti, Lorenzo & Zhang, Jisheng & Zheng, Yuan & Fernandez-Rodriguez, E., 2021. "The influence of waves propagating with the current on the wake of a tidal stream turbine," Applied Energy, Elsevier, vol. 290(C).
    3. Zhang, Zhi & Zhang, Yuquan & Zheng, Yuan & Zhang, Jisheng & Fernandez-Rodriguez, Emmanuel & Zang, Wei & Ji, Renwei, 2023. "Power fluctuation and wake characteristics of tidal stream turbine subjected to wave and current interaction," Energy, Elsevier, vol. 264(C).
    4. Yanxin Xu & Dongjian Zheng & Chenfei Shao & Sen Zheng & Hao Gu, 2023. "Structural Modal Parameter Identification Method Based on the Delayed Transfer Rate Function under Periodic Excitations," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
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