GEP and MLR approaches for the prediction of reference evapotranspiration
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- @Article{mattar:NCaA,
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author = "Mohamed A. Mattar and A. A. Alazba",
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title = "{GEP} and {MLR} approaches for the prediction of
reference evapotranspiration",
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journal = "Neural Computing and Applications",
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year = "2019",
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volume = "31",
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number = "10",
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pages = "5843--5855",
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month = oct,
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keywords = "genetic algorithms, genetic programming, gene
expression programming, Evapotranspiration, Linear
regression, Penman-Monteith",
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ISSN = "0941-0643",
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URL = "http://link.springer.com/article/10.1007/s00521-018-3410-8",
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DOI = "doi:10.1007/s00521-018-3410-8",
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size = "13 pages",
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abstract = "In this study, reference evapotranspiration (ETo) is
modelled as one of the major items of hydrological
applications from different combinations of climatic
variables using two different techniques: gene
expression programming (GEP) and multiple linear
regression (MLR). The data used in modelling were
collected from weather stations in Egypt through the
CLIMWAT database. The Penman Monteith FAO-56 equation
was considered as a reference target for ETo values
depending on the entire climatic variables. The
developed ETo models performances were compared and
evaluated with regard to their predictive abilities
using statistical criteria to identify the superiority
of one modeling approach over the others and determine
climatic variables which have a significant effect on
ETo. The results indicated that GEP and MLR models
contribution toward mean relative humidity and wind
speed at 2 m height is greater compared to that of
other variables. Meanwhile, when adding temperature
data to models, solar radiation has a slight effect on
increasing the accuracy of ETo estimate. Moreover, the
lower statistical error criteria values of GEP models
confirmed their better performance than MLR models and
other empirical equations.",
- }
Genetic Programming entries for
Mohamed A Mattar
A A Alazba
Citations