A fluctuation data grey model and its prediction of rainstorm days.

Rainstorm is one of the most severe and frequent meteorological disasters. The rainstorm event brings considerable risks to the natural ecosystem and human life. There is a limited body of research on applying the grey prediction model for predicting rainstorm-related data. Based on the extreme precipitation index defined by the Expert Team on Climate Change Detection and Indices, this paper uses the data of rainstorm days from 493 weather stations in the middle and lower reaches of the Yangtze River to establish a prediction model. Improving the accuracy of rainstorm days prediction can bring valuable results to decision-makers in a specific region. The annual rainstorm days series exhibits randomness, irregular nonlinearity, fluctuations, etc.; however, the traditional grey prediction model can only identify the trend of a series but not its fluctuations, making its prediction extremely challenging. Therefore, this paper introduces a new dynamic grey action quantity based on the traditional grey model, and the parameters are solved by the hybrid technique of the genetic algorithm and the constrained fmincon function. The prediction results of the new model are compared with those of the traditional grey model, the seasonal grey model, the neural network, and the seasonal autoregressive integrated moving average. In terms of prediction accuracy and similarity of fluctuations, the results demonstrate that the new model outperforms these models. • A fluctuation data grey model is proposed to predict the rainstorm days. • A novel dynamic grey action quantity is introduced into the traditional grey model. • The nonlinear parameters are determined based on hybrid optimization algorithm. • The new model has better simulation and prediction accuracy than the traditional models. [ABSTRACT FROM AUTHOR]