Volume 11 Issue 1
Jan.  2018
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Article Navigation >  Water Science and Engineering  > 2018  >  11(1): 61-67
Jin-ping He, Zhen-xiang Jiang, Cheng Zhao, Zheng-quan Peng, Yu-qun Shi. 2018: Cloud-Verhulst hybrid prediction model for dam deformation under uncertain conditions. Water Science and Engineering, 11(1): 61-67. doi: 10.1016/j.wse.2018.03.002
Citation: Jin-ping He, Zhen-xiang Jiang, Cheng Zhao, Zheng-quan Peng, Yu-qun Shi. 2018: Cloud-Verhulst hybrid prediction model for dam deformation under uncertain conditions. Water Science and Engineering, 11(1): 61-67. doi: 10.1016/j.wse.2018.03.002
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Cloud-Verhulst hybrid prediction model for dam deformation under uncertain conditions

doi: 10.1016/j.wse.2018.03.002

  • a School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430072, China b State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China c Large Dam Safety Supervision Center, National Energy Administration, Hangzhou 311122, China

Funds:  This work was supported by the National Natural Science Foundation of China (Grant No. 51379162) and the Water Conservancy Science and Technology Innovation Project of Guangdong Province (Grant No. 2016-06).
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  • Author Bio:

    whuhjp@163.com (Jin-ping He)

  • Corresponding author: whuhjp@163.com (Jin-ping He)
  • Received Date: 2017-01-23
  • Rev Recd Date: 2017-09-07
    Abstract
  • Uncertainties existing in the process of dam deformation negatively influence deformation prediction. However, existing deformation prediction models seldom consider uncertainties. In this study, a cloud-Verhulst hybrid prediction model was established by combing a cloud model with the Verhulst model. The expectation, one of the cloud characteristic parameters, was obtained using the Verhulst model, and the other two cloud characteristic parameters, entropy and hyper-entropy, were calculated by introducing inertia weight. The hybrid prediction model was used to predict the dam deformation in a hydroelectric project. Comparison of the prediction results of the hybrid prediction model with those of a traditional statistical model and the monitoring values shows that the proposed model has higher prediction accuracy than the traditional statistical model. It provides a new approach to predicting dam deformation under uncertain conditions.

     

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