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

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.