A SVR-ANN combined model based on ensemble EMD for rainfall prediction

Abstract Accurate and timely rainfall prediction is very important in hydrological modeling. Various prediction methods have been proposed in recent years. In this work, information regarding the short-to-long time variation inside original rainfall time series is explored using Ensemble Empirical Mode Decomposition (EEMD) based analysis on three rainfall datasets collected by meteorological stations located in Kunming, Lincang and Mengzi, Yunnan Province, China. Considering both with prediction accuracy and time efficiency, a novel combined model based on the information extracted with EEMD is then proposed in this paper. This model adopts various supervised learning methods for different components of input data, which employs Support Vector Regression (SVR) for short-period component prediction, while Artificial Neural Network (ANN) for long-period components prediction. Our research shows better performances than traditional methods that provides new thinking in rainfall prediction area.

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