Abstract: Reference crop evapotranspiration (ET0) is a basic property to calculate crop water requirement and irrigation scheduling. The weather forecast measurable variables and ET0 calculated by Penman Monteith (PM) formula were used as input to develop the BP neural network model and ANFIS adaptive neural fuzzy inference system model. The estimated value of the two models and the ET0 calculated by the PM formula was not significantly different, showing significant correlation and overall degree of agreement. The two models were compared with the same data samples. The MRE value of BP-ET0 prediction was 32.13%, RMSE was 0.134 mm, and R2 was up to 0.971, which shows that the model has high prediction accuracy and good stability. Compared with the ANFIS-ET0 test results, the root mean square error of BP-ET0 model (0.134 mm/d) was smaller than that of ANFIS-ET0 model (0.188 mm/d) which means that the prediction accuracy was higher for the former model. The average relative error of the ANFIS-ET0 model (16.92%) was less than that of the BP-ET0 model (32.13%) which showed higher stability of the ANFIS-ET0 model. The input items of the two forecast models can be obtained from the current short-term weather forecast factors without special measuring equipment and the procedure is simple, thus it has high practical value and provides a theoretical basis for real-time irrigation forecasting.