Machine Learning Models for Simulating Daily Reference Evapotranspiration in a Semi-Arid Environment Using Four Meteorological Variables: A Multi-Station Study in Northwestern Algeria (Tlemcen Region)

In this study, we evaluated the use of five different ML algorithms (CatBoost, XGBoost, random forest, gradient boosting, and support vector regression SVR) to estimate daily ET0 based only on four independent variables: 2 m air temperature, vapor pressure deficit, 10 m wind speed, and sunshine duration. We used a total of 9132 daily values (2000–2025) from the Open-Meteo Historical Weather API (2000–2025) at 10 stations in the Tlemcen province of northwest Algeria. The dataset was divided into training, validation, and testing sets using a chronological split of 70/15/15. We estimated the performance of each algorithm by using several statistics (RMSE, MAE, R2, NSE, RSR, and Willmott Index) as well as some statistics to evaluate the potential of overfitting and the ability to reproduce the behavior observed during the training phase. CatBoost had the highest overall accuracy and the most generalized performance, with an RMSE of approximately 0.292 mm day−1, MAE of approximately 0.208 mm day−1, R2 of 0.971, and NSE of 0.971 in the test set, suggesting an extremely low risk of overfitting. The optimal CatBoost model was also used to estimate the spatial and temporal variations of monthly ET0. The results showed high interannual variability (changes from year to year from −12.815 to +8.707 mm month−1) in the semi-arid region of Tlemcen but no significant long-term trends (cumulative net change of approximately −0.021 mm month−1 over 2000–2025). Therefore, the use of CatBoost is recommended as a robust, efficient, and reliable emulator of the FAO-56 Penman–Monteith equation (ET0) for estimating ET0 in semi-arid environments with limited climate data availability, and could be particularly useful in northwestern Algeria and other semi-arid Mediterranean regions.