Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data

Detailed meteorological data required for the equation of FAO-56 Penman-Monteith (P-M) method
that was adopted by Food and Agriculture Organization (FAO) as a standard method in estimating
reference evapotranspiration (ETo) are not often available, especially in developing countries. The
Hargreaves equation (HG) has been successfully used in some locations to estimate ETo where
sufficient data were not available to use the P-M method. This paper investigates the potential of two
Artificial Neural Network (ANN) architectures, the multilayer perceptron architecture, in which a backpropagation
algorithm (BPANN) is used, and the cascade correlation architecture (CCANN), in which
Kalman’s learning rule is embedded in modeling the daily ETo with minimal meteorological data. An
overview of the features of ANNs and traditional methods such as P-M and HG is presented, and the
advantages and limitations of each method are discussed. Daily meteorological data from three
automatic weather stations located in Greece were used to optimize and test the different models.
The exponent value of the HG equation was locally optimized, and an adjusted HGadj equation was
used. The comparisons were based on error statistical techniques using P-M daily ETo values as
reference. According to the results obtained, it was found that taking into account only the mean,
maximum and minimum air temperatures, the selected ANN models markedly improved the daily
ETo estimates and provided unbiased predictions and systematically better accuracy compared with
the HGadj equation. The results also show that the CCANN model performed better than the
BPANN model at all stations.