Water Science and Engineering 2018, 11(1) 61-67 DOI:   https://doi.org/10.1016/j.wse.2018.03.002  ISSN: 1674-2370 CN: 32-1785/TV

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Keywords
Dam deformation prediction
Cloud model
Verhulst model
Uncertainty
Inertia weight
Authors
PubMed

Cloud-Verhulst hybrid prediction model for dam deformation under uncertain conditions

Jin-ping He a,b,*, Zhen-xiang Jiang a, Cheng Zhao c, Zheng-quan Peng a, Yu-qun Shi a

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

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.

Keywords Dam deformation prediction   Cloud model   Verhulst model   Uncertainty   Inertia weight  
Received 2017-01-23 Revised 2017-09-07 Online: 2018-01-31 
DOI: https://doi.org/10.1016/j.wse.2018.03.002
Fund:

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).

Corresponding Authors: whuhjp@163.com (Jin-ping He)
Email: whuhjp@163.com
About author: whuhjp@163.com (Jin-ping He)

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