Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Estimation of Reference Crop Evapotranspiration Using Backpropagation Neural Network Model

역전파 신경망 모델을 이용한 기준 작물 증발산량 산정

  • Kim, Minyoung (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Choi, Yonghun (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • O'Shaughnessy, Susan (Conservation and Production Research Laboratory, United States Department of Agriculture, Agricultural Research Service (USDA-ARS)) ;
  • Colaizzi, Paul (Conservation and Production Research Laboratory, United States Department of Agriculture, Agricultural Research Service (USDA-ARS)) ;
  • Kim, Youngjin (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Jeon, Jonggil (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Lee, Sangbong (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA))
  • Received : 2019.10.01
  • Accepted : 2019.11.08
  • Published : 2019.11.30
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Abstract

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

작물 증발산량은 수자원 계획 및 관리, 물수지 분석, 작물 관개 계획 및 생산량 추정 등에 널리 활용되고 있으며, 특히 FAO에서 공인한 Penman-Monteith식 (FAO 56-PM)은 잠재 증발산량 산정을 위한 표준방법으로 많이 사용되고 있다. Penman-Monteith식을 이용한 잠재증발산량 산정은 최소온도, 평균온도, 최대온도, 상대습도, 풍속과 일사량인 6가지 항목에 대한 시계열 자료가 필요한데, 결측 또는 미계측된 경우에는 사용이 어려운 단점을 가지고 있다. 따라서, 본 연구에서는 역전파 신경망(BPNN) 모델을 이용해서 6개 미만의 기상항목으로도 잠재증발산량이 추정가능한지를 확인하였다. 여섯 가지 기상항목을 각각 1~6개의 조합으로 입력자료를 구성하고, BPNN 모델을 이용해서 학습, 검증 및 테스트를 한 결과, 입력 자료가 많아질수록 좋은 결과가 산출되었으며, 일사량, 최대온도와 상대습도만으로도 결정계수($R^2$)가 0.94정도로 비교적 높은 예측결과를 얻을 수 있었다. 또한 산정 오차를 줄이고, 항목간의 상관관계를 높이기 위해서는 역전파 신경망 구조의 적절한 선택이 중요한 것으로 확인되었다. 역전파 신경망 모델을 사용하면 요구되는 기상 항목과 데이터의 양에 대한 제약 없이 예측이 가능할 수 있기 때문에 기준 증발산량 산정에 유용하게 활용될 수 있을 것이며 향후 작물 재배를 위한 적정 관개계획 수립에도 유용하게 사용될 것이라 사료된다.

Keywords

References

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