농촌계획 (Journal of Korean Society of Rural Planning)

  • 제28권4호
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  • Pages.105-117
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  • 2022
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  • 1225-8857(pISSN)
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  • 2288-9493(eISSN)
한국농촌계획학회 (Korean Society of Rural Planning)
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머신러닝 기법을 활용한 논 순용수량 예측

Prediction of Net Irrigation Water Requirement in paddy field Based on Machine Learning

  • 김수진 (서울대학교 그린바이오과학기술연구원 ) ;
  • 배승종 (서울대학교 그린바이오과학기술연구원 ) ;
  • 장민원 (경상국립대학교 지역시스템공학과)
  • Kim, Soo-Jin (Institute of Green Bio Science and Technology, Seoul National University) ;
  • Bae, Seung-Jong (Institute of Green Bio Science and Technology, Seoul National University) ;
  • Jang, Min-Won (Department of Agricultural Engineering, Institute of Agriculture and Life Science, Gyeongsang National University)
  • 투고 : 2022.11.18
  • 심사 : 2022.11.23
  • 발행 : 2022.11.30
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초록

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R2 of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

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과제정보

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. NRF-2019R1A2C1010125).

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