Assessment of machine learning models for estimating daily reference evapotranspiration in semi-arid region.

Evapotranspiration (ET) has been the focus of research for decades as it is the key to improving water resources management. This study addresses the estimation of daily reference evapotranspiration (ET₀) using machine learning (ML) models with limited meteorological data in newly developed agricultural areas in Egypt. The study tested five methods for selecting key input features to compare the performance of 20 ML models across 14 stations using available daily meteorological data from 1943 to 2024. The modified FAO Penman-Monteith (PM) method was used to estimate ET₀ as a benchmark for evaluating the performance of ML models. The Categorical Boosting (CatBoost) regressor achieved the highest performance across all stations when using the complete feature dataset and maintained high performance with three features. Furthermore, the results showed consistent success of CatBoost, Light Gradient Boosting Machine (LightGBM), and Gradient Boosting Machine (GB) when two features, selected by Sequential Forward Selection (SFS) method, were used. This highlights the feature selection method's ability to capture the most important meteorological variables, making it a valuable tool for improving ET₀ prediction in different agroclimatic regions. In addition, wind speed (U) and maximum temperature (Tmax) were identified as critical predictors of daily ET₀ in Egypt. This approach offers a practical solution for improving water management in newly reclaimed desert areas, not only in Egypt but also in other regions with similar climatic conditions.