Artificial Neural Networks (ANNs) has been used in
prediction of reference evapotranspiration for a recent decade.
Its performance is competitive to a widely used method the
so-called “Penman-Monteith” method. In this study, we aim to
estimate the crop evapotranspiration by ANNs from climatic data
in Thailand and compare the performance with the PenmanMonteith
method. As missing data is inevitable, we also included
the missing data situation into the study. This can be solved
by expectation-maximization algorithm. The accuracy of the
prediction decreases when the amount of missing values increases.
Furthermore, we exploit the feature selection in the study. It
shows that sunshine duration is the most important feature
followed by temperature and wide speed, respectively.