Deformation Prediction of Dam Based on Optimized Grey Verhulst Model

Abstract

Dam deformation monitoring data are generally characterized by non-smooth and no-saturated S-type fluctuation. The grey Verhulst model can get better results only when the data series is non-monotonic swing development and the saturated S-shaped sequence. Due to the limitations of the grey Verhulst model, the prediction accuracy will be limited to a certain extent. Aiming at the shortages in the prediction based on the traditional Verhulst model, the optimized grey Verhulst model is proposed to improve the prediction accuracy of the dam deformation monitoring. Compared with those of the traditional GM (1,1) model, the DGM (2,1) model, and the traditional Verhulst (1,1) model, the experimental results show that the new proposed optimized Verhulst model has higher prediction accuracy than the traditional gray model. This study offers an effective model for dealing with the non-saturated fluctuation sequence to predict dam deformation under uncertain conditions.