Modeling potential evapotranspiration by improved machine learning methods using limited climatic data

HIGHLIGHTS

• who: Reham R. Mostafa et al. from the Information Systems Department, Faculty of Computers and Information Sciences, University, Department of Civil Engineering, Technical University of Lu00fcbeck, Lu00fcbeck, Germany have published the paper: Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data, in the Journal: Water 2023, 486 of /2023/
• what: This study investigates the efficiency of two machineu2011learning methods random vector functional link (RVFL) and relevance vector machine (RVM) improved with new metaheuristic algorithms quantumu2011based avian navigation optimizer algorithm (QANA) and artificial hummingbird algorithm (AHA) in modeling ET0 using limited climatic data . . .
  

   

  

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