Journal of Yangtze River Scientific Research Institute ›› 2022, Vol. 39 ›› Issue (7): 59-65.DOI: 10.11988/ckyyb.20210276


A Deformation Prediction Model for Concrete Dam Based on Extreme Learning Machine Optimized by Variable Selection

CAO En-hua1,2,3, BAO Teng-fei1,2,3,4, HU Shao-pei3, YUAN Rong-yao3, YAN Tao1,2,3   

  1. 1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing 210098,China;
    2. National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety,Hohai University,Nanjing 210098,China;
    3. College of Water-conservancy and Hydropower,Hohai University,Nanjing 210098,China;
    4. College of Hydraulic & Environmental Engineering,Three Gorges University,Yichang 443002,China
  • Received:2021-03-27 Revised:2021-08-20 Online:2022-07-01 Published:2022-07-25

Abstract: Traditional statistical models are of weak generalization capability and are prone to introduce high-dimensional variables,which will negatively affect the output of neural network-based prediction models and increase the risk of overfitting.It is necessary to build a data-driven model with appropriate dimensionality to accomplish accurate monitoring of dam deformation.In this paper,extreme learning machine(ELM)is selected as the base prediction model,and a variable selection method based on mean impact value(MIV)-ELM model is proposed to eliminate redundant information in the initial variable set,thus reducing the model's complexity and improving the prediction accuracy.Analysis results demonstrate that compared with traditional prediction models,HST-MIV-ELM not only has the highest prediction accuracy and robustness,but also has strong scalability.The study provides a reliable theoretical basis for the construction of dam safety monitoring system.

Key words: deformation prediction for concrete dam, variable selection, extreme learning machine, mean impact value, reverse variable-by-variable elimination method

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