한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제60권6호
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  • Pages.43-54
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  • 2018
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  • 1738-3692(pISSN)
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  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
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기준 일증발산량 산정을 위한 인공신경망 모델과 경험모델의 적용 및 비교

Comparison of Artificial Neural Network and Empirical Models to Determine Daily Reference Evapotranspiration

  • Choi, Yonghun (Department of Agricultural Engineering, National Institute of Agricultural Sciences(NAS), Rural Development Administration(RDA)) ;
  • Kim, Minyoung (Department of Agricultural Engineering, National Institute of Agricultural Sciences(NAS), Rural Development Administration(RDA)) ;
  • O'Shaughnessy, Susan (Conservation and Production Research Laboratory, USDA Agricultural Research Service (USDA-ARS)) ;
  • Jeon, Jonggil (Department of Agricultural Engineering, National Institute of Agricultural Sciences(NAS), Rural Development Administration(RDA)) ;
  • Kim, Youngjin (Department of Agricultural Engineering, National Institute of Agricultural Sciences(NAS), Rural Development Administration(RDA)) ;
  • Song, Weon Jung (Sangju Agricultural Technology Center)
  • 투고 : 2018.07.17
  • 심사 : 2018.09.27
  • 발행 : 2018.11.30
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초록

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

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