An Effective Hybrid Semi-parametric Regression Strategy for Artificial Neural Network Ensemble and Its Application Rainfall Forecasting

Rainfall forecasting is very important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors including linear and nonlinear pattern. Recently there are lots of novel forecasting approaches of improving the forecasting accuracy. Artificial neural network, which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi parametric regression ensemble (SRE) model was presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression model based on ANN are able to capture rainfall nonlinear characteristics. Then the semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results obtained reveal that the prediction using the SRE model is generally better than those obtained using the other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the SRE model proposed here can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality further.