Abstract:Different combinations of meteorological factors, including monthly maximum temperature, monthly minimum temperature, monthly average temperature, average wind speed, sunshine duration, and relative humidity were used as the input data, the results calculated by the FAO Penman-Monteith equation were used as the calibration values, and a PSO-LSSVM model based on the least squares support vector machine(LSSVM)and particle swarm optimization(PSO)was established for prediction of ET0. Meteorological data from the Habahe Meteorological Station in the Irtysh River Basin over the period from 1986 to 2013 were used to train and test the model, and the results calculated by the PSO-LSSVM model were compared with those calculated by other commonly used ET0 calculation formulas. The results show that the PSO-LSSVM model can reflect the non-linear relationships between ET0 and the meteorological factors well, and that temperature is the most important factor that influences the accuracy of simulation. However, as the number of meteorological factors decreases, the accuracy of simulation will decrease. When the calculation is based on radiation and temperature conditions, the PSO-LSSVM model has higher accuracy than the Priestley-Taylor and Hargreaves-Samani equations. The PSO-LSSVM model, with multi-factor quantitative indicators, is both precise and practical, providing scientific references for ET0 study in areas that lack data.