Meteorological Prediction Using Support Vector Regression with Genetic Algorithms

The theory of support vector regression (SVR) is introduced in this paper. And genetic algorithms (GAs) are adopted to optimize free parameters of support vector regression. Then we develop an optimal meteorological prediction model based on support vector Regression with genetic algorithms (SVRG). In this study, SVRG is applied to predict meteorology. The experimental results indicate that SVRG model proposed in this paper overcomes some shortcomings of the traditional SVR, and can achieve better forecasting accuracy and performance than traditional SVR and BP neural network (BPNN) prediction models. Consequently, the meteorological prediction model based on SVRG is an effective method.