基于FOA-FSVM的RF模式台风期阵风精细化预报

For the sake of improving the accuracy of forecasting wind speed during typhoon strike in WRF model forecast, a new method for precision forecasting of typhoon wind speed is proposed by combining the data collected from the WRF model forecast and an automatic observation station. The method incorporates many factors influencing the typhoon wind speed. The wind speed which is obtained using the traditional human prediction produces large error when compared with actual wind speed. To address this issue, a fuzzy support vector regression model for wind forecasting is built. Considering the fact that the fuzzy support vector regression model is not adequately efficient in determining the punishment factor and kernel parameter, the fly optimization algorithm is introduced into optimizing the parameters of the fuzzy support vector machine. According to the characteristics of the wind speed regression, the fruit fly optimization algorithm is developed in three dimensional space, combining with the enhancement factor γ for improving the global optimization ability of traditional fruit fly optimization algorithm. The results show that the forecasting wind speed and the actual one is in good agreement with each other, and the correlation is as high as 99％. The presented method of wind speed prediction provides higher accuracy than that of traditional FOA-FSVM model and FOA-SVM model.