The amount of water withdrawn from the surface of the earth in developing countries is estimated as ~ 81%, while the same is ~ 71% globally. Most of the readily available fresh water on the earth’s surface is used up by the agricultural sector. The difficulty to access water has become one of the fundamental issues globally through the twenty-first century (Gleeson et al. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET 0 values, as compared to other selected algorithms. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. From the results, it was revealed that all the selected models predicted ET 0 at both stations with greater precision. The results of performed regression analysis on all input parameters proved that T min, RH Avg, U x, and n were identified as the most influential input parameters at the studied station. Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET 0 modeling. The climatic dataset includes maximum and minimum temperature ( T max, T min), average relative humidity (RH avg), average wind speed ( U x), and sunshine hours ( n). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET 0). For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance.

Weka 3.8.1 download