Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Study on the Estimation of Frost Occurrence Classification Using Machine Learning Methods

기계학습법을 이용한 서리 발생 구분 추정 연구

  • 김용석 (국립농업과학원 농업환경부 기후변화생태과) ;
  • 심교문 (국립농업과학원 농업환경부 기후변화생태과) ;
  • 정명표 (국립농업과학원 농업환경부 기후변화생태과) ;
  • 최인태 (국립농업과학원 농업환경부 기후변화생태과)
  • Received : 2017.08.21
  • Accepted : 2017.09.23
  • Published : 2017.09.30
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Abstract

In this study, a model to classify frost occurrence and frost free day was developed using the digital weather forecast data provided by Korea Meteorological Administration (KMA). The minimum temperature, average wind speed, relative humidity, and dew point temperature were identified as the meteorological variables useful for classification frost occurrence and frost-free days. It was found that frost-occurrence date tended to have relatively low values of the minimum temperature, dew point temperature, and average wind speed. On the other hand, relatively humidity on frost-free days was higher than on frost-occurrence dates. Models based on machine learning methods including Artificial Neural Network (ANN), Random Forest(RF), Support Vector Machine(SVM) with those meteorological factors had >70% of accuracy. This results suggested that these models would be useful to predict the occurrence of frost using a digital weather forecast data.

본 연구에서는 기상청 예보자료를 이용할 것을 전제로 서리가 발생하는 날과 서리가 발생하지 않는 날을 구분하는 모형을 구축하였다. 서리가 발생한 날과 서리가 발생하지 않은 날을 구분할 수 있는 기상인자로서 최저기온, 평균풍속, 평균상대습도, 평균이슬점온도로 나타났다. 기상인자별로 두 날을 비교한 결과 서리가 발생한 날이 서리가 발생하지 않은 날에 비해 최저기온, 이슬점온도, 평균풍속은 낮게 나타났고 상대습도는 높게 나타났다. 이러한 기상인자로 인공신경망, 랜덤포레스트, 서포트벡터 머신의 기계학습법을 이용한 모형을 연구한 결과 70%이상의 정확도를 나타내어 활용 가능성이 있을 것으로 판단된다.

Keywords

References

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