Real-time prediction of daily reference crop evapotranspiration (ET0) is the basis for estimating crop evapotranspiration and for computing crop irrigation requirements. In recent years, least-squares support vector machines (LSSVMs) have been applied for forecasting in many fields of engineering. In this paper, LSSVMs are applied to forecast ET0 using public weather forecasting data (minimum and maximum temperature, average relative humidity, wind scale and weather conditions). LSSVM-estimated ET0 is compared with Penman–Monteith (PM)-estimated ET0 using measured meteorological data. Based on a comparison between LSSVM and PM over a 2 month period, the results show that the root mean square error and mean absolute error are less than 0.5 and 0.4 mm d−1, respectively, and that the model efficiency is greater than 90%. This indicates that ET0 can be successfully estimated using public weather forecasts through the LSSVM approach.

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