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인공신경망을 이용한 기상관측장비 결측 보완 기술에 관한 연구

A Study of the Method for Estimating the Missing Data from Weather Measurement Instruments

  • 민재식 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 이무훈 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 지준범 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 장민 (한국외국어대학교 차세대도시농림융합기상사업단)
  • Min, Jae-Sik (Weather Information Service Engine Institute, Hankuk University of Foreign Studies) ;
  • Lee, Moo-Hun (Weather Information Service Engine Institute, Hankuk University of Foreign Studies) ;
  • Jee, Joon-Bum (Weather Information Service Engine Institute, Hankuk University of Foreign Studies) ;
  • Jang, Min (Weather Information Service Engine Institute, Hankuk University of Foreign Studies)
  • 투고 : 2016.06.28
  • 심사 : 2016.08.20
  • 발행 : 2016.08.28

초록

본 연구는 현재 운영 중인 자동기상관측장비인 ASOS와 AWS의 결측에 대해 안공신경망을 활용하여 주변 관측값을 기반으로 결측을 보완하기 위한 연구이다. 2011년부터 2015년까지 수집된 서울지역 기온, 습도, 풍속을 대상으로 학습데이터를 구성하고 인공신경망을 통해 학습모델을 구축하였으며, 서울관측소를 결측으로 가정하고 학습 모델에 대한 검증을 수행하였다. 학습횟수 증가에 따른 민감도 실험 결과 초기종료는 학습횟수 2,000회에서 나타났다. 관측과 추정치의 상관관계는 모든 기상변수에서 0.6이상이었으며 기온과 습도의 경우 각각 0.9, 0.8 이상의 높은 상관성을 보였다. RMSE는 대부분 기상변수에 대해 학습횟수가 증가함에 따라 꾸준히 감소하지만 풍속의 경우 뚜렷한 증감 경향이 나타나지 않았다. 학습시간은 학습횟수가 증가할수록 지수함수적으로 증가하는 경향을 보였다. 학습 횟수 40회의 ANN 성능은 초기종료 시점까지 향상된 결과에 80%이상의 효과를 볼 수 있으며 2초 내의 빠른 학습시간으로 신속한 결측 보완을 통해 보다 상세한 기상정보의 활용이 가능할 것으로 기대된다.

과제정보

연구 과제 주관 기관 : 기상청

참고문헌

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피인용 문헌

  1. A Missing Value Replacement Method for Agricultural Meteorological Data Using Bayesian Spatio–Temporal Model vol.27, pp.7, 2018, https://doi.org/10.5322/JESI.2018.27.7.499