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

• Main Authors: Jin-ping He, Zhen-xiang Jiang, Cheng Zhao, Zheng-quan Peng, Yu-qun Shi
• Format: Article
• Language: English
• Published: Elsevier 2018-01-01
• Series: Water Science and Engineering
• Online Access: http://www.sciencedirect.com/science/article/pii/S1674237018300206

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