Predicting reference evapotranspiration in semi-arid-region by regression- based machine learning methods using limited climatic inputs

Abstract Accurately estimation of evapotranspiration is very essential for water resources planning and management projects. In this study, 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 in order to model daily reference evapotranspiration (ET0) for semi-arid region. Five stations in Hemren catchment basin located at the North-East part of Iraq were selected as case study. Several climatic parameters including solar radiation (SR), wind speed (Us), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin) of 35 years (1979–2014) period were used as inputs to the models. Assessment of the methods with various input combinations indicated that the RF method especially with Tmax, Tmin, Tmean & SR inputs provided the best accuracy in estimating daily ET0 in all stations. It was followed by the BaT and BoT methods while the SVM had the worst accuracy. In some cases, 1st input scenario (Tmax, Tmin, Tmean, SR, WS and RH) provided slightly better accuracy than the 2nd input scenario (Tmax, Tmin, Tmean & SR).