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http://dx.doi.org/10.5391/JKIIS.2015.25.3.242

Design of Meteorological Radar Echo Classifier Based on RBFNN Using Radial Velocity  

Bae, Jong-Soo (School of Electrical and Electronics Engineering, The University of Suwon)
Song, Chan-Seok (School of Electrical and Electronics Engineering, The University of Suwon)
Oh, Sung-Kwun (School of Electrical and Electronics Engineering, The University of Suwon)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.25, no.3, 2015 , pp. 242-247 More about this Journal
Abstract
In this study, we propose the design of Radial Basis Function Neural Network(RBFNN) classifier in order to classify between precipitation and non-precipitation echo. The characteristics of meteorological radar data is analyzed for classifying precipitation and non-precipitation echo. Input variables is selected as DZ, SDZ, VGZ, SPN, DZ_FR, VR by performing pre-processing of UF data based on the characteristics analysis and these are composed of training and test data. Finally, QC data being used in Korea Meteorological Administration is applied to compare with the performance results of proposed classifier.
Keywords
RBFNN; Echo Judgement Module; Fuzzy C-Mean Clustering; Echo Classifier; Precipitation; Non-precipitation; UF data;
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