Potential of RT, bagging and RS ensemble learning algorithms for reference evapotranspiration prediction using climatic data-limited humid region in Bangladesh

[Display omitted] •First reports on the ensemble learning (EL) algorithms for reference evapotranspiration (ETo) prediction in Bangladesh.•Random tree (RT) model outperforms other models to estimate daily ETo.•The RT and RF models have suggested as the promising potentials for daily ETo estimate.•Wind speed and solar radiation are the highest contributing variables influencing the observed and predicted ETo changes. Ensemble learning (EL), an alternative approach in the machine-learning field, offers an accurate reference evapotranspiration (ETo) prediction, which is of paramount significance for the hydrological studies and agricultural water practices. Although the FAO-56 Penman-Monteith (PM) equation is regarded as an ideal model for estimating ETo, its applicability is limited due to the absence of required climatic datasets in many regions of the world. Despite its significance, only a few studies use the EL algorithms for the ETo prediction perspective. In this study, we contribute to fill this gap from a two-fold way. First, we present the potential of new EL Random Tree (RT), Bagging and Random Subspace (RS) algorithms which were compared with two commonly used Random Forest (RF), and Support Vector Machine (SVM) algorithms for predicting daily ETo with the climatic data-limited humid region in Bangladesh during 1983–2017 using a five-fold cross-validation scheme. Second, we explore the role of contributing variables influencing ETo change at the regional scale. When a lack of climatic datasets, Tmax (maximum temperature), Tmin (minimum temperature) and Rs (solar radiation) as 3 input combinations obtained the reasonable precision for estimating ETo in all climatic regions except for south-central and south-western regions. Compared to other state-of-arts models, the RT model performed superior to predict ETo in all input combinations followed by the RF, Bagging, RS, and SVM. Considering less difficulty level, high prediction accuracy, more dependability and fewer computation costs of studied models, RT and RF models have been suggested as the promising potentials for daily ETo estimate in subtropical climate regions of Bangladesh and also may be applicable worldwide in the similar climates. The importance analysis from the RF model depicted that the wind speed (U2) and solar radiation (Rs) are the largest influential variables affecting the observed and predicted daily ETo changes in Bangladesh.