Abstract: The traditional GM(1,1) model can be employed to forecast spring drought in Suzhou with small amount of data,but this model is not ideal due to the large scope of sequence changes.The gray and BP neural network model were adopted to predict the years of the serious spring drought occurrence in Suzhou.The scope of data sequence was weakened and the disposal of differential coefficient of GM(1,1) model was improved to build near-precision model m-GM(1,1) and to revise the residual error of m-GM(1,1) model.The result showed that the precision of the gray neural network model(|Q|=0.0045) was much higher than that of the single 1.7-GM(1,1) model(|Q|=4.18) and the traditional single GM(1,1) model(|Q|=9.36).The year of 2009 would be the next serious spring drought year after 2005 forecasted by the model.

Key words: Suzhou, Suzhou, Serious spring drought, M-GM(1, 1) model, BP neural network