Daily Rainfall Prediction with SVR Using a Novel Hybrid PSO-SA Algorithms

Abstract

AbstractIn this study, a novel hybrid evolutionary algorithm is proposed to improve the regression accuracy of support vector regression (SVR) based on the Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms, called SVR–PSO–SA. This optimization mechanism combined PSO with SA to simultaneously optimize the type of kernel function and the kernel parameter setting of SVR. It is troublesome to escape from the local optima for multi–objective optimization. To avoid premature convergence of PSO, this paper present a new hybrid evolutionary algorithm based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local–search ability. The proposed the hybrid evolutionary algorithm was applied to optimize all parameter setting of SVR for the performance of SVR. The SVR–PSO–SA model is tested at daily rainfall forecasting in Guangxi, China. The results showed that the new SVR–PSO–SA model outperforms the traditional SVR models. Specifically, the new SVR–PSO–SA model can successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting.KeywordsParticle Swarm OptimizationSupport Vector RegressionMean Absolute Percentage ErrorNormalize Mean Square ErrorSupport Vector Regression ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.