Improvement and application of grey model for meteorological drought prediction

In view of the shortcomings of the traditional Grey Model ( 1, 1) ( GM ) ( 1, 1) in the data prepr ocessing stage, a new data preprocessing method was proposed by analyzing the causes of model errors. Based on the annual precipitation data of Xiajiang County from 1958 to 2018, drought year time series was obtained by calculating SPI index ( standardized precipitation index ) . The classical gray model GM0 , the translational conversion preprocessing gray model GM 1 , and the average weakening buffer operator pr eprocessing gray model GM 2 based on translation transformation were compared on the real-time series data set. The results showed that the translational conversion combined with the average weakening buffer operator compensated the shortcomings of the prepr ocessing stage and effectively reduced the error of the traditional model. The average prediction error of the improved GM 2 model w as 3.32% , which was 44.16 and 16.24 percentage points lower than the other two models. It is proved that the model has better prediction accuracy . It can be applied to the prediction of dry years, providing a theoretical basis for regional drought pr ediction and drought control.

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