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이은정 ( Eunjeong Lee ),박정안 ( Jeongan Park ),최진영 ( Jinyoung Choi ),강문성 ( Moonseong Kang ),박승우 ( Seungwoo Park ) 한국농공학회 2009 한국농공학회 학술대회초록집 Vol.2009 No.-
Evapotranspiration (ET) is one of the basic component of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration (ET0) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, in Korea. Four climate factors, daily maximum temperature (T<sub>max</sub>), minimum temperature (T<sub>min</sub>), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 A1B scenario. The evapotranspirations were 549.72 mm/yr in baseline period (1973~2008), 558.08 mm/yr in 2011-2030 (2020s), 593.03 mm/yr in 2046-2065 (2055s), and 641.07 mm/yr in 2080-2099 (2090s). The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.