Estimation of Reference Evapotranspiration in Semi-Arid Region with Limited Climatic Inputs Using Metaheuristic Regression Methods

Abstract

Different regression-based machine learning techniques, including support vector machine (SVM), random forest (RF), Bagged trees algorithm (BaT), and Boosting trees algorithm (BoT) were adopted for modeling daily reference evapotranspiration (ET0) in a semi-arid region (Hemren catchment basin in Iraq). An assessment of the methods with various input combinations of climatic parameters, including solar radiation (SR), wind speed (WS), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin), indicated that the RF method, especially with Tmax, Tmin, Tmean, and SR inputs, provided the best accuracy in estimating daily ET0 in all stations, while the SVM had the worst accuracy. This work will help water users, developers, and decision makers in water resource planning and management to achieve sustainability.

Keywords

• climatic inputs
• evapotranspiration
• Hemren catchment
• machine learning
• prediction
• semi-arid region