ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 38 Issue 9 > 2012,38(9):1115-1120. DOI:10.7519/j.issn.1000-0526.2012.09.011 Prev Next
Application of Support Vector Machine to Thunderstorm Forecasting
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Abstract:
In the paper the K means clustering of the improved algorithm, the principal component analysis (PCA) and other methods are used to establish the interpretation forecasting model of thunderstorm by the least squares support vector machine (LS_SVM) and linear programming support vector machine (LP_SVM) based on MOS theory monthly in terms of AREM prediction products and conventional observation data during 2002 to 2006. And use the data at Haikou Station for testing from May to August 2007. The results show that, combining with SVM and AREM products to interpret the forecast products is feasible. The PCA also plays a positive role in improving the forecast accuracy.
Keywords:
thunderstorm, AREM model, interpretation forecast, support vector machine, principal component analysis
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