Adaptive Neurofuzzy Computing Technique for Evapotranspiration Estimation
Publication: Journal of Irrigation and Drainage Engineering
Volume 133, Issue 4
Abstract
The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration is investigated in this paper. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, Calif., are used as inputs to the neurofuzzy model to estimate obtained using the FAO-56 Penman–Monteith equation. In the first part of the study, a comparison is made between the estimates provided by the neurofuzzy model and those of the following empirical models: The California Irrigation Management System, Penman, Hargreaves, and Ritchie. In this part of the study, the empirical models are calibrated using the standard FAO-56 PM values. The estimates of the neurofuzzy technique are also compared with those of the calibrated empirical models and artificial neural network (ANN) technique. Mean-squared errors, mean-absolute errors, and determination coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neurofuzzy models could be employed successfully in modeling the process. In the second part of the study, the potential of the neurofuzzy technique, ANN and the empirical methods in estimation using nearby station data are investigated.
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Acknowledgments
The data used in this study were downloaded from the California Irrigation Management Information System (CIMIS) web server. The author wishes to thank the staff of the CIMIS who are associated with data observation, processing, and management of CIMIS Web sites.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 133 • Issue 4 • August 2007
Pages: 368 - 379
Copyright
© 2007 ASCE.
History
Accepted: May 3, 2007
Published online: Aug 1, 2007
Published in print: Aug 2007
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