Metabolic grey early warning model for dam deformation based on wavelet denoising

Abstract

Influenced by environment and human factors, the observed data of dam deformation consist of real deformation value and observation error (noise). The conventional GM(1,1) model based on nondenoised observation data is not very effective. In order to improve the prediction effect of conventional GM(1,1) model, wavelet threshold denoising method is used to eliminate the noise in the original data and improve the smoothness of the data sequence. Then, based on the conventional GM(1,1) model, the metabolic GM(1,1) model is established by eliminating the oldest information and adding the newest information. The application results show that the wavelet threshold denoising can obviously remove the noise from the original data. The predicted vertical displacement of the metabolic GM(1,1) model based on the denoised data has little difference with the measured value, and the predicted precision is obviously higher than that of the conventional GM (1,1) model. Therefore, the metabolic GM(1,1) model based on wavelet denoising can be used for prediction and early warning of dam deformation.