Support vector machine enhanced empirical reference evapotranspiration estimation with limited meteorological parameters

Abstract

In regions where economies that are highly dependent on agricultural activities, estimation of evapotranspiration is vital for scheduling and managing the water resources. In this study, the estimation of the reference evapotranspiration (ET0) was performed for the west coast of Peninsular Malaysia, where oil palm is the major crop. Three stations, namely Station 48603 (Alor Setar), Station 48620 (Sitiawan) and Station 48650 (KLIA Sepang) were selected representing different regions along the west coast. Estimation of ET0 using the popular and conventional Penman-Monteith (PM) model is data intensive. On the contrary, simpler empirical models such as the temperature-based Hargreaves-Samani (HS), mass transfer-based Romanenko and radiation-based Makkink models are less accurate, unstable and are greatly affected by local climate conditions. In order to solve the problems of the PM and other simple empirical models, the SVM was used to mimic the simple empirical models. The results of this study revealed that the HS and Makkink models were favorable for ET0 estimation at the northern region, whereas for the central region, the Makkink model was not suitable. Simple averaging of the empirical models improved the estimations. However, the model was still deemed to be poor as the MARE ranged from 0.087 to 0.099, with the R2 still being possible to dip below 0.900. It was found that the SVM could greatly improve the ET0 estimation. Unlike the empirical models, the regional factor had minimal effect on the ET0 estimation; where the SVM-Mak model performed better than the SVM-HS and SVM-Rom models over the whole study area. The best performance was achieved by SVM-Mak model at Station 48620 (Sitiawan) where the MARE was only 0.020 when compared to the chosen standard PM model. It was found that the simple averaging of the SVM models could further boost the performance, but such an approach was non-rewarding. The SVM models were found to be better alternatives for the empirical models when using similar inputs. The study concluded that the SVM model which imitated the Makkink model was suitable for ET0 estimation along the west coast of Peninsular Malaysia.