A Novel Combined Prediction Model for Monthly Mean Precipitation With Error Correction Strategy

Abstract

Precipitation is an important parameter of water resource management, flood warning and hydrological analysis, so it is important to predict rainfall accurately. However, many previous studies did not extract the information of error series and only used a single model to predict rainfall data, ignoring the importance of model stability. Therefore, based on the idea of combination prediction and error correction strategy, this paper proposes a novel combined prediction model for monthly mean precipitation. It combines the variational mode decomposition (VMD), the improved butterfly optimization algorithm (IBOA), the least squares support vector machine model (LSSVM), the adaptive Volterra and autoregressive moving average (ARMA) model. Firstly, in order to find the best parameters of LSSVM, an improved butterfly optimization algorithm is proposed. The simulation results show the performance of IBOA is better than that of other algorithms, such as PSO, DE and BOA. Then the IBOA-LSSVM model and Volterra model are established for the mode components of the VMD, named VMD-IBOA-LSSVM and VMD-Volterra. Secondly, to solve the problem that the uncertainty of the hydrological prediction model, a combined precipitation prediction method based on the induced ordered weighted average (IOWA) operator of VMD-IBOA-LSSVM and VMD-Volterra is proposed. Finally, the ARMA model is established to correct the error sequence of the combined forecasting model. The precipitation data of two stations in Shaanxi Province are predicted. Experiment 1 is taken as an example, the maximum error of the proposed prediction model for rainfall is less than 9 mm, and the performance of the proposed model is improved by at least 43%. It shows that the proposed model can effectively reduce the prediction error of precipitation, and provide a new idea for precipitation prediction.

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