Soft computing approaches for forecasting reference evapotranspiration
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- @Article{Gocic:2015:CEA,
-
author = "Milan Gocic and Shervin Motamedi and
Shahaboddin Shamshirband and Dalibor Petkovic and Sudheer Ch and
Roslan Hashim and Muhammad Arif",
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title = "Soft computing approaches for forecasting reference
evapotranspiration",
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journal = "Computers and Electronics in Agriculture",
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volume = "113",
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pages = "164--173",
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year = "2015",
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ISSN = "0168-1699",
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DOI = "doi:10.1016/j.compag.2015.02.010",
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URL = "http://www.sciencedirect.com/science/article/pii/S0168169915000526",
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abstract = "Accurate estimation of reference evapotranspiration
(ET0) is needed for planning and managing water
resources and agricultural production. The FAO-56
Penman-Monteith equation is used to determinate ET0
based on the data collected during the period 1980-2010
in Serbia. In order to forecast ET0, four soft
computing methods were analysed: genetic programming
(GP), support vector machine-firefly algorithm
(SVM-FFA), artificial neural network (ANN), and support
vector machine-wavelet (SVM-Wavelet). The reliability
of these computational models was analysed based on
simulation results and using five statistical tests
including Pearson correlation coefficient, coefficient
of determination, root-mean-square error, absolute
percentage error, and mean absolute error. The
end-point result indicates that SVM-Wavelet is the best
methodology for ET0 prediction, whereas SVM-Wavelet and
SVM-FFA models have higher correlation coefficient as
compared to ANN and GP computational methods.",
-
keywords = "genetic algorithms, genetic programming, Soft
computing, Forecasting, Firefly algorithm, Support
vector machine, Wavelet, Serbia",
- }
Genetic Programming entries for
Milan Gocic
Shervin Motamedi
Shahaboddin Shamshirband
Dalibor Petkovic
Sudheer Ch
Roslan Hashim
Muhammad Arif Syed Hamid
Citations