Modeling Reference Evapotranspiration Using Evolutionary Neural Networks
Publication: Journal of Irrigation and Drainage Engineering
Volume 137, Issue 10
Abstract
The ability of evolutionary neural networks (ENN) to model reference evapotranspiration () was investigated in this study. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed of three stations in central California, Windsor, Oakville, and Santa Rosa, were used as inputs to the ENN models to estimate obtained using the FAO-56 Penman-Monteith equation. In the first part of the study, a comparison was made between the estimates provided by the ENN and those of the following empirical models: the California Irrigation Management System, Penman, Hargreaves, modified Hargreaves, and Ritchie methods. Root-mean-squared error, coefficient of efficiency, and correlation coefficient statistics were used as comparing criteria for the evaluation of the models’ accuracies. The ENN performed better than the empirical models. In the second part of the study, the ENN results were compared with those of the conventional artificial neural networks (ANN). The comparison results revealed that the ENN models were superior to ANN in modeling the process.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 137 • Issue 10 • October 2011
Pages: 636 - 643
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Jun 22, 2010
Accepted: Dec 22, 2010
Published online: Dec 27, 2010
Published in print: Oct 1, 2011
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