Application of stepwise regression-time series and RBF-ANN models to precipitation forecasting
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Abstract:
With integration of stepwise regression into the foundation of time series analysis model, the traditional mode of “take into account all the variables” is abandoned and just significant variables are used to establish the prediction equation in the form of “both enter and exit” mode, with the distinction of each factor’s major and minor relationship. The radial basis function artificial neural network (RBF-ANN) belongs to partial approaches network and has high accuracy. Take Huadian County’s month precipitation as an example, and compare the accuracy of prediction equations which are established using traditional, stepwise regression time series analysis model and RBF-ANN. The results show that the posterior error ratios of the traditional time series, stepwise regression time series and RBF-ANN models are 0.315, 0.272 and 0.284, the average absolute errors are 18.37 mm, 15.65 mm and 13.82 mm, and the effective coefficients are 0.87, 0.94 and 0.93. At last, we forecast the precipitation and evaporation in future three years with the stepwise regression time series analysis model.
