evaluation of five data mining algorithms in predicting monthly potential evapotranspiration (case study: shiraz)

Data mining algorithms were used in this study to predict shiraz's monthly potential evapotranspiration. the cart (classification and regression trees), m5p, k-star, m5rules, and rep-tree (reduced error pruning tree) algorithms were used to predict potential evapotranspiration. meteorological data from the shiraz weather station from 2001 to 2016 were used in this study. the cart algorithm performed better in estimating monthly averages, according to statistical indicators. the maximum amount of potential evapotranspiration was reached when the sunshine hours exceeded 9.5 hours and the wind speed exceeded 0.3 meters per second, according to the results. when there was less than 9.5 hours of sunshine and the air temperature was less than 2 °c, the potential evapotranspiration rate was the lowest. the sensitivity analysis revealed that the parameters of sunshine hours, air temperature, wind speed, and relative humidity had a positive effect on the cart algorithm's performance in estimating monthly evapotranspiration.