American Society of Civil Engineers


Generalization of ETo ANN Models through Data Supplanting


by Pau Martí, (Ph.D. Researcher, Centro Valenciano de Estudios sobre el Riego (CVER), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain. E-mail: paumarpe@doctor.upv.es), Alvaro Royuela, (Professor, Centro Valenciano de Estudios sobre el Riego (CVER), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain. E-mail: aroyuela@agf.upv.es), Juan Manzano, (Professor, Centro Valenciano de Estudios sobre el Riego (CVER), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain. E-mail: juamanju@agf.upv.es), and Guillermo Palau-Salvador, (Professor, Departamento de Ingeniería Rural y Agroalimentaria, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain. E-mail: guipasal@agf.upv.es)

Journal of Irrigation and Drainage Engineering, Vol. 136, No. 3, March 2010, pp. 161-174, (doi:  http://dx.doi.org/10.1061/(ASCE)IR.1943-4774.0000152)

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Document type: Journal Paper
Abstract: This paper describes the application of artificial neural networks (ANNs) for estimating reference evapotranspiration (ETo) 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 ETo 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 ETo prediction, proposed by the FAO as the sole standard method for ETo 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.


ASCE Subject Headings:
Neural networks
Predictions
Data analysis
Irrigation
Evapotranspiration

Author Keywords:
Artificial neural networks
ETo prediction
Data supplanting