Eltarabily, Mohamed Galal ， Elshaarawy, Mohamed Kamel ， Bali, Khaled M. ， Gabr, Mohamed Elsayed
2025-10-01 JOURNAL OF WATER AND CLIMATE CHANGE 2025 16(卷), 10(期), (3082-3108页)
Accurate estimation of reference evapotranspiration (ETo) is essential for efficient agricultural water management, especially in arid and semi-arid regions where climate variability limits traditional methods. This study evaluates the FAO Penman-Monteith (PM) equation and five machine learning (ML) models using a 5-year (2019-2023) dataset from two California stations: Parlier (Mediterranean) and Meloland (desert). Among 31 input combinations, CatBoost outperformed others, achieving an R-2 of 0.995 and a root mean squared error (RMSE) of 0.161 mm/day at Parlier, and an R-2 of 0.991 and an RMSE of 0.211 mm/day at Meloland. Using only mean temperature and net radiation, CatBoost maintained strong performance (R-2: 0.932-0.948; RMSE: 0.507-0.589 mm/day). Bayesian optimization enhanced model tuning and reduced overfitting. Compared with random forest and XGBoost, CatBoost reduced RMSE by 5-12% and generalized well across sites. Violin boxplots confirmed its stability and accuracy. A user-friendly graphical interface was also developed for real-time ETo estimation. These results highlight the promise of ML-based ETo prediction tools for practical, scalable use in data-limited environments.