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

作     者：Jin-ping He Zhen-xiang Jiang Cheng Zhao Zheng-quan Peng Yu-qun Shi 

作者机构：School of Water Resources and Hydropower EngineeringWuhan UniversityWuhan 430072China State Key Laboratory of Water Resources and Hydropower Engineering ScienceWuhan UniversityWuhan 430072China Large Dam Safety Supervision CenterNational Energy AdministrationHangzhou 311122China 

出 版 物：《Water Science and Engineering》 (水科学与水工程（英文版）)

年 卷 期：2018年第11卷第1期

页      面：61-67页

核心收录：

学科分类：081504[工学-水利水电工程] 08[工学] 0815[工学-水利工程] 

基　　金：supported by the National Natural Science Foundation of China(Grant No.51379162) the Water Conservancy Science and Technology Innovation Project of Guangdong Province(Grant No.2016-06) 

主　　题：Dam deformation prediction Cloud model Verhulst model Uncertainty Inertia weight 

摘      要：Uncertainties existing in the process of dam deformation negatively influence deformation prediction. However, existing deformation pre- diction 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 sta- tistical model. It provides a new approach to predicting dam deformation under uncertain conditions.