Accurate forecasting of reference evapotranspiration (ET$_o$) is essential for sustainable water resource management and precision agriculture, yet systematic comparisons across extended forecasting horizons remain limited. This study evaluates three ET$_o$ prediction methods---Random Forest (RF), Cartesian Genetic Programming (CGP), and Convolutional Neural Network accelerated by Graphics Processing Unit (CNN-GPU)---across six time intervals ranging from 1 to 364 days, using data from two semi-arid stations in northwestern Iran. Model performance was assessed via dispersion analysis (scatter and violin plots) and four accuracy metrics (RMSE, MAE, R$^2$, SI). Results indicate that RF and CNN... More >