Comparison of artificial neural networks and prediction models for reference evapotranspiration estimation in a semi-arid region

Abstract

Estimation of reference evapotranspiration (ETo) is essential for determination of crop water requirements. In this research, Penman–FAO (P-FAO) and Penman–Monteith (PM) equations were calibrated and validated by lysimeter-measured ETo with six and four weather parameters. Furthermore, two input structures (six and four weather parameters) to artificial neural networks (ANNs) were investigated. Results showed that the accuracy of the PM equation is greater than that of the P-FAO equation. An empirical equation was developed to estimate daily ETo using mean daily temperature and relative humidity, and sunshine hours. The accuracy of the equation to estimate daily ETo using smooth weather data is greater than that of an equation using original data. Furthermore, ANNs were able to estimate ETo properly. The accuracy of ANNs with six inputs is higher than that obtained using the P-FAO equation and is similar to that determined using the PM equation. A decrease in number of inputs to ANNs generally decreased the accuracy of estimation, however, ANNs were able to estimate ETo properly when wind speed and solar radiation were unavailable. Furthermore, the accuracy of ANNs, with four input parameters is greater than that obtained using the PM equation and is similar to that obtained with P–FAO and the developed empirical equations.