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기준 일증발산량 산정을 위한 인공신경망 모델과 경험모델의 적용 및 비교
최용훈 ( Yonghun Choi ),김민영 ( Minyoung Kim ),수잔오샤네시 ( Susan O’shaughnessy ),전종길 ( Jonggil Jeon ),김영진 ( Youngjin Kim ),송원정 ( Weon Jung Song ) 한국농공학회 2018 한국농공학회논문집 Vol.60 No.6
The accurate estimation of reference crop evapotranspiration (ET<sub>o</sub>) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of ET<sub>o</sub> 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<sub>o</sub> 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<sub>o</sub> was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of ET<sub>o</sub> and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against ET<sub>o</sub> 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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