The Research Of Gray Markov Chain Prediction Method And Its Application On Hydrological Series

Firstly the article introduces the current situation of the methods of predicting the hydrology array .There are many prediction method in predicting hydrology array . The article mainly introduces two kinds of prediction methods: Markov chain prediction method and the gray prediction method. Then gray Markov model is described.When using one kind of method to forecast, accepting the method advantage,and accepting its shortcoming. Mostly people often handle in advance and correct residual to optimize model. It is difficult to shake off inherent limitation of one method, although these measure have a certain value. Its main characteristic is that the results are desirable ,when predicting short and monotone data series,and is not advantaged to predict randomness and great changed data. The article introduces some methods of improved GM(1,1),and advances GM(1,1) residual gray model, GM(1,1) nonequal interval residual gray model, GM(1,1) residual Markov chain model which means gray Markov chain model, GM(1,1) nonequal interval residual gray Markov chain model.It shows that the predicting effect of gray Markov chain model is the best through contrasting them. The second is the GM(1,1) nonequal interval residual gray Markov chain model.Gray model forecasting curve shows the developing of hydrology array, Markov model reflect the volatility law and then optimize the result.the scope of predicted value is given .By the combination of the two mentioned above,the practical problem is solved.