Forecasting Long-Series Daily Reference Evapotranspiration Based on Best Subset Regression and Machine Learning in Egypt

The estimation of reference evapotranspiration (ETo), a crucial step in the hydrologic cycle, is essential for system design and management, including balancing, planning, scheduling agricultural water supply resources. When climates vary from arid to semi-arid, there are problems with lack meteorological data future information on ETo, as case Egypt, it more important estimate ETo precisely. To address this, current study aimed model Egypt’s most governorates (Al Buhayrah, Alexandria, Ismailiyah, Minufiyah) using four machine learning (ML) algorithms: linear regression (LR), random subspace (RSS), additive (AR), reduced error pruning tree (REPTree). Climate Forecast System Reanalysis (CFSR) National Centers Environmental Prediction (NCEP) was used gather daily climate variables 1979 2014. datasets were split into two sections: training phase, i.e., 1979–2006, testing 2007–2014. Maximum temperature (Tmax), minimum (Tmin), solar radiation (SR) found be three input that had influence outcome subset sensitivity analysis. A comparative analysis ML models revealed REPTree outperformed competitors by achieving best values various performance matrices during phases. study’s novelty lies use predict this algorithm has not been commonly purpose. Given sparse attempts such research, remarkable accuracy predicting highlighted rarity study. In order combat effects aridity through better resource also cautions authorities concentrate their policymaking adaptation.