Generalization of ${\text{ET}}_{\mathrm{o}}$ ANN Models through Data Supplanting

This paper describes the application of artificial neural networks (ANNs) for estimating reference evapotranspiration $\left({\text{ET}}_{\mathrm{o}}\right)$ as a function of local maximum and minimum air temperatures as well as exogenous relative humidity and reference evapotranspiration in different continental contexts of the autonomous Valencia region, on the Spanish Mediterranean coast. The development of new and more precise models for ${\text{ET}}_{\mathrm{o}}$ prediction from minimum climatic data is required, since the application of existing methods that provide acceptable results is limited to those places where large amounts of reliable climatic data are available. The Penman-Monteith model for ${\text{ET}}_{\mathrm{o}}$ prediction, proposed by the FAO as the sole standard method for ${\text{ET}}_{\mathrm{o}}$ estimation, was used to provide the ANN targets for the training and testing processes. Concerning models which demand scant climatic inputs, the proposed model provides performances with lower associated errors than the currently existing temperature-based models, which only consider local data.