Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Design of Meteorological Radar Echo Classifier Based on RBFNN Using Radial Velocity

시선속도를 고려한 RBFNN 기반 기상레이더 에코 분류기의 설계

  • 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)
  • Received : 2015.03.22
  • Accepted : 2015.05.26
  • Published : 2015.06.25
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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.

본 논문은 방사형 기저함수 신경회로망(Radial Basis Function Neural Network) 패턴분류기를 기반으로 강수 에코와 비(非)강수 에코를 분류하는 방법을 제시한다. 강수 에코와 비(非)강수 에코를 분류하기 위하여 기상레이더 자료의 특성을 분석하였다. 이를 기반으로 UF 데이터의 전처리를 실시하여 입력변수(DZ, SDZ, VGZ, SPN, DZ_FR, VR)를 선정 하였고 학습데이터 및 테스트데이터로 구성하였다. 마지막으로, 기상청에서 사용되고 있는 QC 데이터는 제안된 알고리즘의 성능을 비교하기 위해 사용하였다.

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