Application of integrated ARIMA and RBF network for groundwater level forecasting

Abstract

The combination model of autoregressive integrated moving average (ARIMA) and radial basis function network (RBFN) is used for the prediction of monthly groundwater level. The ARIMA model is used to estimate the linear principal of time series, and the RBFN model is used to predict the nonlinear residuals. The proposed hybrid model is applied to forecast the monthly groundwater level fluctuations for two observation wells in the city of Xi’an, China. The monthly groundwater level data from the year 1998 to 2008 are used for training the applied models and the data from the year 2009 to 2010 are reserved for testing. Predicted data from the hybrid model are compared with those from the ARIMA model and RBFN model using the accuracy measures. The result shows that the proposed hybrid model has both the good linear fitting ability of ARIMA model and the great nonlinear mapping ability of RBFN model. The prediction accuracy rate is higher than that of any single model. Therefore, the application of the combination model in the prediction of groundwater level is effective and feasible.