A Prediction Algorithm for a Heavy Rain Newsflash using the Evolutionary Symbolic Regression Technique

진화적 기호회귀 분석기법 기반의 호우 특보 예측 알고리즘

  • Hyeon, Byeongyong (Dept. of Electronics Engineering, Seokeyong University) ;
  • Lee, Yong-Hee (National Institute of Meteorological Research/Korea Meteorological Administration) ;
  • Seo, Kisung (Dept. of Electronics Engineering, Seokeyong University)
  • 현병용 (서경대학교 전자공학과) ;
  • 이용희 (국립기상연구소 예보연구과) ;
  • 서기성 (서경대학교 전자공학과)
  • Received : 2013.11.18
  • Accepted : 2014.05.05
  • Published : 2014.07.01


This paper introduces a GP (Genetic Programming) based robust technique for the prediction of a heavy rain newsflash. The nature of prediction for precipitation is very complex, irregular and highly fluctuating. Especially, the prediction of heavy precipitation is very difficult. Because not only it depends on various elements, such as location, season, time and geographical features, but also the case data is rare. In order to provide a robust model for precipitation prediction, a nonlinear and symbolic regression method using GP is suggested. The remaining part of the study is to evaluate the performance of prediction for a heavy rain newsflash using a GP based nonlinear regression technique in Korean regions. Analysis of the feature selection is executed and various fitness functions are proposed to improve performances. The KLAPS data of 2006-2010 is used for training and the data of 2011 is adopted for verification.


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