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2019 Vol.61, Issue 6 Preview Page
역전파 신경망 모델을 이용한 기준 작물 증발산량 산정

Estimation of Reference Crop Evapotranspiration Using Backpropagation Neural Network Model

Minyoung Kima
,
Yonghun Choib†
,
O’Shaughnessy Susanc
,
Colaizzi Pauld
,
Youngjin Kime
,
Jonggil Jeonf
,
Sangbong Leeg
김 민영a
,
최 용훈b†
,
수잔 오샤네시c
,
폴 콜레이지d
,
김 영진e
,
전 종길f
,
이 상봉g
a농림 과학 기술원 국립 농업 과학원 농업 공학과 농업 연구원
aAgricultural Researcher, Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)
b농촌 진흥청 국립 농업 과학원 농공학과 박사후 연구원
bPost-doctoral researcher, Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)
c미국 농업 농업 연구 서비스국 (USDA-ARS)의 보존 및 생산 연구 실험실 농업 엔지니어
cAgricultural Engineer, Conservation and Production Research Laboratory, United States Department of Agriculture, Agricultural Research Service (USDA-ARS)
d미국 농업 농업 연구 서비스국 (USDA-ARS)의 보존 및 생산 연구 실험실 농업 엔지니어
dAgricultural Engineer, Conservation and Production Research Laboratory, United States Department of Agriculture, Agricultural Research Service (USDA-ARS)
e농림 과학 기술원 국립 농업 과학원 농업 공학과 농업 연구원
eAgricultural Researcher, Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)
f농림 과학 기술원 국립 농업 과학원 농업 공학과 농업 연구원
fAgricultural Researcher, Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)
g농림 과학 기술원 국립 농업 과학원 농업 공학과 농업 연구원
gAgricultural Researcher, Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)
† Corresponding author Tel.: +82-63-238-4161 Fax: +82-63-238-4145 E-mail: yhchoi82@korea.kr
2019. pp. 111-121
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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, ETo). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole ETo 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 ETo 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 ETo. The overall findings of this study indicated that ETo 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.

Keywords
Reference crop evapotranspiration
Penman-Monteith equation (FAO 56-PM)
Backpropagation neural network (BPNN) model
meteorological variables

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 (R2)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.

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Information
  • Publisher :The Korean Society of Agricultural Engineers
  • Publisher(Ko) :한국농공학회
  • Journal Title :Journal of Korean Society of Agricultural Engineers
  • Journal Title(Ko) :한국농공학회논문집
  • Volume : 61
  • No :6
  • Pages :111-121
  • Received Date : 2019-10-01
  • Revised Date : 2019-11-07
  • Accepted Date : 2019-11-08
  • DOI :https://doi.org/10.5389/KSAE.2019.61.6.111
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