A coupled extreme gradient boosting-MPA approach for estimating daily reference evapotranspiration

Reference evapotranspiration (ET₀) modeling is pivotal for irrigation scheduling and water resources planning. This study presents a hybrid approach integrating Extreme Gradient Boosting (XGB) with Marine Predators Algorithm (MPA) for daily ET₀ estimation in northern Algeria. The proposed XGB-MPA model was evaluated against traditional empirical models and assessed using statistical methods. Shapley Additive Explanations (SHAP) was employed to enhance model interpretability. Various combinations of meteorological variables were tested as inputs, including air temperature, relative humidity, sunshine hours, wind speed, and extraterrestrial solar radiation. The XGB-MPA hybrid model achieved superior prediction accuracy during testing (R² = 0.9958, RMSE = 0.1713 mm/day) compared to traditional empirical models and the standard XGB model. The study demonstrated that ET₀ prediction accuracy increased with the number of meteorological inputs used. Our findings highlight the XGB-MPA hybrid model's potential for accurate ET₀ estimation in northern Algeria, which can be used for water resource management and irrigation planning.