Artificial neural network for estimating monthly reference evapotransiration under arid and semi arid environments

The objective of this study is to investigate the potential of artificial neural networks (ANNs) for estimating reference monthly evapotranspiration under arid and semi-arid environments. A simple leave one out data analysis was carried out; one neural network solution on six inputs and another six...

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Bibliographic Details
Published in:Archiv für Acker- und Pflanzenbau und Bodenkunde 2013-01, Vol.59 (1), p.105-117
Main Author: Mohawesh, Osama E.
Format: Article
Language:English
Subjects:
Accuracy
Air temperature
Arid environments
Environmental assessment
Evapotranspiration
Hargreaves equation
Irrigation scheduling
Irrigation water
Jordan valley
Neural networks
Relative humidity
Semiarid environments
Sensitivity analysis
Studies
Water resources management
Wind speed
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Summary:The objective of this study is to investigate the potential of artificial neural networks (ANNs) for estimating reference monthly evapotranspiration under arid and semi-arid environments. A simple leave one out data analysis was carried out; one neural network solution on six inputs and another six network solutions on five inputs for each monitoring station were done. Comparison of the results showed that the accuracy of ANNs is decreased when relative humidity, wind speed and solar or extraterrestrial radiation are excluded as input variables. The results also showed that monthly evapotranspiration could be computed with relatively good accuracy compared with local calibrated Hargreaves equation based on air temperature using trained ANNs at another location. We conclude, based on our overall results, that temperature-based method ANNs can be used with relatively good accuracy for water resource management, irrigation scheduling and management, and environmental assessment when data are not enough using trained ANNs from another location.
ISSN:0365-0340
1476-3567
DOI:10.1080/03650340.2011.603126