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Comparison of Aqueduct Deformation Prediction Based on Linear Additive Models
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Shu-sen DENG1, Zhao-hui ZHU1, 2, Hao WU1, 2, Zi-wen WANG1, 2
Water Resources and Power | 2025, 43(9) : 123 - 126
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Water Resources and Power | 2025, 43(9): 123-126
Comparison of Aqueduct Deformation Prediction Based on Linear Additive Models
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Shu-sen DENG1, Zhao-hui ZHU1, 2, Hao WU1, 2, Zi-wen WANG1, 2
Affiliations
  • 1.China Institute of Water Resources and Hydropower Research, Beijing 100048, China
  • 2.Beijing IWHR Technology Co., Ltd., Beijing 100048, China
Published: 2025-09-25 doi: 10.20040/j.cnki.1000-7709.2025.20241994
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Aqueducts are common water conveyance structures in water diversion projects, and accurate prediction of aqueduct deformation is crucial for ensuring the stable operation of water conservancy projects. For this purpose, taking the Liaohe Aqueduct in the South-to-North Water Diversion Project as an example, five different linear additive models, namely elastic net regression, multiple linear regression, stepwise regression, ridge regression and LASSO regression, were established based on the long-term deformation monitoring data of the aqueduct. The prediction results of the aqueduct's deformation behavior by the five different linear additive models were compared. The results indicate that as the prediction time increases, the prediction accuracy of different linear additive models gradually decreases. The LASSO model selects the optimal regularization parameter through cross-validation, achieving variable selection simplification and minimizing model complexity. Additionally, it is verified that the training length affects the prediction performance of multiple linear regression and stepwise regression. The findings of this study provide valuable references for selecting prediction model of aqueduct deformation.

aqueduct  /  deformation  /  prediction  /  linear additive model
Shu-sen DENG, Zhao-hui ZHU, Hao WU, Zi-wen WANG. Comparison of Aqueduct Deformation Prediction Based on Linear Additive Models[J]. Water Resources and Power, 2025 , 43 (9) : 123 -126 . DOI: 10.20040/j.cnki.1000-7709.2025.20241994
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Article Info
doi: 10.20040/j.cnki.1000-7709.2025.20241994
  • Receive Date：2024-10-23
  • Online Date：2025-12-15
  • Published：2025-09-25
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  • Received：2024-10-23
  • Revised：2024-11-25
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    1.China Institute of Water Resources and Hydropower Research, Beijing 100048, China
    2.Beijing IWHR Technology Co., Ltd., Beijing 100048, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2025.20241994
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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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