Resumen

SATTARI, Mohammad Taghi; AHMADIFAR, Vahdat; DELIRHASANNIA, Reza  y  APAYDIN, Halit. Estimation of the pan evaporation coefficient in cold and dry climate conditions via the M5 regression tree model. Atmósfera [online]. 2021, vol.34, n.3, pp.289-300.  Epub 04-Oct-2021. ISSN 0187-6236.  https://doi.org/10.20937/atm.52777.

In this study, class A pan coefficient (K p ) values were simulated via the M5 tree model, by using daily meteorological data of four stations in the East Azerbaijan province, which has arid and cold climate in the northwest of Iran. Firstly, the FAO-24 and FAO-56 methods, which are commonly used to calculate K p values, were taken into consideration in the study. The K p values calculated in the second stage were assumed to be observed values and were taken as the outputs of the M5 model. Four different training datasets consisting of 66, 70, 75 and 80% of the original data were tested. The best results were obtained when 70% of the data was used for training and 30% for testing. Results indicated that a K p value was easily simulated with simple linear equations with high accuracy rate (R2 = 0.99) in all the stations. Furthermore, the K p value was easily simulated using only two meteorological variables (relative humidity and wind speed), without the need for complex tables and equations. The most important finding of this study was the easy estimation of the K p with a number of linear functions obtained from the M5 model; as a result, the simulated K p can help us to calculate evapotranspiration accurately for more effective irrigation planning. The proposed method offers advantages as it is simpler and easier than the existing approaches in the literature.

Palabras llave : class a pan; data mining; decision tree; evapotranspiration; pan coefficient.