Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Design of Heavy Rain Advisory Decision Model Based on Optimized RBFNNs Using KLAPS Reanalysis Data

KLAPS 재분석 자료를 이용한 진화최적화 RBFNNs 기반 호우특보 판별 모델 설계

  • Kim, Hyun-Myung (Department of Electrical Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (Department of Electrical Engineering, The University of Suwon) ;
  • Lee, Yong-Hee (Department of Forecast Research Division, The National Institute of Meteorological Research)
  • 김현명 (수원대학교 전기공학과) ;
  • 오성권 (수원대학교 전기공학과) ;
  • 이용희 (국립기상연구소 예보연구과)
  • Received : 2013.03.31
  • Accepted : 2013.09.05
  • Published : 2013.10.25
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Abstract

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

본 논문에서는 KLAPS(Korea Local Analysis and Prediction System)의 재분석 자료를 이용하여 지능형 뉴로-퍼지 알고리즘 RBFNNs(Polynomial-based Radial Basis Function Neural Networks) 기반 호우특보 판별 모델을 개발한다. 기존의 호우예측 시스템들의 예측능력은 일반적으로 기상데이터의 가공 기법의 영향을 받는다. 본 연구에서는 이를 보완하기 위하여 기상데이터의 전처리를 통한 호우예측 방법을 소개한다. 기상 데이터 전처리 기법은 KLAPS 데이터를 기반으로 지점별 변환, 누적강수량 생성, 시계열 데이터 가공, 호우특보 추출 방식에 의하여 설계된다. 최종적으로, 향후 t(t=1,2,3) 시간 후 6시간 동안 누적강수량에 대해 예측하고 호우특보를 결정하기 위한 정보를 제공한다. 또한 다항식의 형태, 규칙의 개수, 퍼지화 계수와 같은 제안된 모델의 중요 파라미터는 최적화 기법인 차분 진화(Differential Evolution; DE)를 이용하여 최적화한다.

Keywords

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