Rainfall study based on ARIMA-RBF combined model

Abstract

The traditional differential integrated moving average autoregressive model (ARIMA) has some deviations in the prediction accuracy of monthly rainfall. In this paper, we propose to combine the ARIMA model with the radial basis function neural network (RBF) neural network model to predict the monthly rainfall in Nanchang, Jiangxi Province, using the ARIMA-RBF model. Firstly, the ARIMA model is used to predict the monthly rainfall and calculate its residuals, and then the RBF neural network model is used to approximate and compensate the prediction results of the ARIMA model to correct the final prediction results. The results show that the prediction results of the combined model are better than those of the single ARIMA model and the single RBF neural network model with good accuracy.