Comparison of three data-driven techniques in modelling the evapotranspiration process
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{El-Baroudy:2010:JH,
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author = "I. El-Baroudy and A. Elshorbagy and S. K. Carey and
O. Giustolisi and D. Savic",
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title = "Comparison of three data-driven techniques in
modelling the evapotranspiration process",
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journal = "Journal of Hydroinformatics",
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year = "2010",
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volume = "12",
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number = "4",
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pages = "365--379",
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keywords = "genetic algorithms, genetic programming, EPR, actual
evapotranspiration, data driven techniques, eddy
covariance, evolutionary polynomial regression, neural
networks",
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ISSN = "1464-7141",
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URL = "http://www.iwaponline.com/jh/012/0365/0120365.pdf",
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DOI = "doi:10.2166/hydro.2010.029",
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size = "15 pages",
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abstract = "Evapotranspiration is one of the main components of
the hydrological cycle as it accounts for more than
two-thirds of the precipitation losses at the global
scale. Reliable estimates of actual evapotranspiration
are crucial for effective watershed modelling and water
resource management, yet direct measurements of the
evapotranspiration losses are difficult and expensive.
This research explores the utility and effectiveness of
data-driven techniques in modelling actual
evapotranspiration measured by an eddy covariance
system. The authors compare the Evolutionary Polynomial
Regression (EPR) performance to Artificial Neural
Networks (ANNs) and Genetic Programming (GP).
Furthermore, this research investigates the effect of
previous states (time lags) of the meteorological input
variables on characterising actual evapotranspiration.
The models developed using the EPR, based on the two
case studies at the Mildred Lake mine, AB, Canada
provided comparable performance to the models of GP and
ANNs. Moreover, the EPR provided simpler models than
those developed by the other data-driven techniques,
particularly in one of the case studies. The inclusion
of the previous states of the input variables slightly
enhanced the performance of the developed model, which
in turn indicates the dynamic nature of the
evapotranspiration process.",
- }
Genetic Programming entries for
Ibrahim El-Baroudy
Amin Elshorbagy
Sean K Carey
Orazio Giustolisi
Dragan Savic
Citations