Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data

Modeling potential evapotranspiration (ET0) is an important issue for water resources planning and management projects involving droughts flood hazards. Evapotranspiration, one of the main components hydrological cycle, highly effective in drought monitoring. This study investigates efficiency two machine-learning methods, random vector functional link (RVFL) relevance machine (RVM), improved with new metaheuristic algorithms, quantum-based avian navigation optimizer algorithm (QANA), artificial hummingbird (AHA) modeling ET0 using limited climatic data, minimum temperature, maximum extraterrestrial radiation. The outcomes hybrid RVFL-AHA, RVFL-QANA, RVM-AHA, RVM-QANA models compared single RVFL RVM models. Various input combinations three data split scenarios were employed. results revealed that AHA QANA considerably methods ET0. Considering periodicity component radiation as inputs prediction accuracy applied methods.