디지털융복합연구 (Journal of Digital Convergence)

  • 제18권4호
  • /
  • Pages.243-250
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  • 2020
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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기계학습과 GloSea5를 이용한 장기 농업기상 예측 : 고랭지배추 재배 지역을 중심으로

The long-term agricultural weather forcast methods using machine learning and GloSea5 : on the cultivation zone of Chinese cabbage.

  • 김준석 (대구대학교 통계학과) ;
  • 양미연 ((재)대구디지털산업진흥원 빅데이터활용센터) ;
  • 윤상후 (대구대학교 수리빅데이터학부)
  • Kim, Junseok (Department of Statistics, Daegu University) ;
  • Yang, Miyeon (Daegu Digital Industry Promotion Agency) ;
  • Yoon, Sanghoo (Division of Mathematics and big data science, Daegu University)
  • 투고 : 2020.02.14
  • 심사 : 2020.04.20
  • 발행 : 2020.04.28
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초록

농작물 재배에 있어 가장 큰 위험 요소는 날씨이므로 재배지의 장기 농업 기상정보를 얻을 수 있다면 정식과 수확 시기 등을 예측할 수 있다. 따라서 체계적인 농작업을 기획하여 관리할 수 있으며 이는 농가의 안정적인 수확으로 이어질 것으로 기대한다. 본 연구는 GloSea5와 기계학습을 이용하여 효과적인 고랭지배추의 재배를 위한 장기 농업기상정보 예측 방법을 제시하였다. GloSea5는 계절예측시스템으로 최대 240일까지의 기상을 예측한다. 심층신경망과 공간랜덤포레스트를 이용하여 장기 일 평균기온을 예측한 결과 심층신경망이 공간랜덤포레스트에 비해 장기예측성능이 우수하였다. 하지만 공간랜덤포레스트는 강원도 전역의 기온을 짧은 시간에 예측하는 장점이 있다. 공간랜덤포레스트로 분석한 결과 여름철과 해발고도가 낮은 지역의 장기 일 평균기온이 잘 예측되었다.

Systematic farming can be planned and managed if long-term agricultural weather information of the plantation is available. Because the greatest risk factor for crop cultivation is the weather. In this study, a method for long-term predicting of agricultural weather using the GloSea5 and machine learning is presented for the cultivation of Chinese cabbage. The GloSea5 is a long-term weather forecast that is available up to 240 days. The deep neural networks and the spatial randomforest were considered as the method of machine learning. The longterm prediction performance of the deep neural networks was slightly better than the spatial randomforest in the sense of root mean squared error and mean absolute error. However, the spatial randomforest has the advantage of predicting temperatures with a global model, which reduces the computation time.

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