A Unified Model for Estimation of Reference Evapotranspiration Using an Assembly of Ensemble Learners Coupled with Swarm Intelligence Optimizers

Several machine learning models and their ensembles have been suggested for reference evapotranspiration (ET0) modeling at different climatic regions. Researchers reported that optimizing model hyperparameters using an intelligent algorithm significantly improves the performance of such models. However, ensemble models hybridized with hyperparameter optimizers have hardly been applied for the precise estimation of ET0 worldwide. The current research is devoted to designing sixteen hybrid versions of four ensemble models, alternatively coupled with four popular swarm intelligence optimization algorithms and finding the best-fit model against different input combinations of available climatic parameters for the groundwater-stressed region of North Bengal, India. The performances of four ensemble models and their sixteen hybrid versions were compared in terms of four well-recognized statistical metrics: the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). Experimental results depicted that in nearly 92% of cases, the hybrid versions outperformed the primary ensemble models, irrespective of the available climatic parameters. In most cases, the ensemble models hybridized with the whale optimization algorithm (WOA) produced the highest estimation accuracy, followed by the sailfish optimizer (SFO). Solar radiation was also found to be the most significant climatic parameter for estimating ET0 in this region.