A Combination of Differential Evolution and Support Vector Machine for Rainstorm Forecast

Author

Jun, Shu ; Jian, Li

Author_Institution

Inst. of Electr. & Electron. Eng., Hubei Univ. of Ind., Wuhan, China

Volume

3

fYear

2009

fDate

21-22 Nov. 2009

Firstpage

392

Lastpage

395

Abstract

This study employed a DE-SVM model that hybridized the differential evolution (DE) and support vector machines (SVM) to improve the classification accuracy for rainstorm forecasting. This optimization mechanism combined the DE to optimize the SVM parameter setting. Based on the European Centre for Medium-Range Weather Forecasts (ECMWF), Japan and T213 precipitation data from 2003 to 2006, using DE-SVM, the 24 hour´s storm models for 5 sub-regions in Hubei province were created, which have been used in the real-time running work from May to July in 2007. The results have shown the forecasting ability and reference value of the SVM method.

Keywords

atmospheric precipitation; geophysics computing; optimisation; pattern classification; support vector machines; weather forecasting; European Centre for Medium-Range Weather Forecasts; SVM parameter setting; classification accuracy; differential evolution; forecasting ability; optimization mechanism; precipitation data; rainstorm forecasting; reference value; support vector machine; Load forecasting; Meteorology; Predictive models; Storms; Support vector machine classification; Support vector machines; Technology forecasting; Weather forecasting; Wind forecasting; Wind speed; differential evolution; rainstorm forecast; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on

Conference_Location

Nanchang

Print_ISBN

978-0-7695-3859-4

Type

conf

DOI

10.1109/IITA.2009.475

Filename

5370237