Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration

Abstract For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input critical importance. In this study, five intelligent hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET 0 ). purpose, two stations located in semi-arid region Pakistan used from period 1987 2016. The dataset includes maximum minimum temperature ( T max , min ), average relative humidity (RH avg wind speed U x sunshine hours n Sensitivity analysis through methods was determine effective parameters for ET modeling. results performed on all proved that RH Avg identified as most influential at studied station. From results, it revealed selected models predicted both greater precision. AR-REPTree furthest AR-M5P nearest observed point based performing indices stations. study concluded under aforementioned methodological framework, can yield higher accuracy predicting values, compared other algorithms.