Abstract: The prediction result of GM(1,1) grey model is subject to be disturbed by outdated information previously measured in the system, while one-dimensional dynamic GM(1,1) model is restrained by the selection of the dimension. To overcome these problems, this paper studies the content and the modeling of Multidimensional Weighted Dynamic GM(1,1) model (MDWD-GM(1,1) model) in detail. Based on the prediction results of all the dimensions calculated by this model, the weight value of each dimension is obtained by Sa function weighting method and BP neural network, then the final predictive value is obtained by weighting. Moreover, the MDWD-GM(1,1) model has been applied to the dam monitoring system and the application manifests that it offers better prediction results than traditional GM(1,1) model and one-dimensional dynamic GM(1,1) model as it takes the effect of different dimensions into account and increases the white degree of the grey range by updating the data in time.

Key words: GM(1,1) model ,  , one-dimensional dynamic GM(1,1) model  , multidimensional weighted dynamic GM(1,1) model ,  , weight  , BP neural network ,  , the Sa function