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

Abstract

The accurate estimation of reference crop evapotranspiration (ETo) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of ETo 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 ETo 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 ETo was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of ETo and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against ETo 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.