A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River
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- @Article{Olyaie:2016:GF,
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author = "Ehsan Olyaie and Hamid Zare Abyaneh and
Ali {Danandeh Mehr}",
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title = "A comparative analysis among computational
intelligence techniques for dissolved oxygen prediction
in {Delaware} River",
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journal = "Geoscience Frontiers",
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year = "2017",
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volume = "8",
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number = "3",
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pages = "517--527",
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keywords = "genetic algorithms, genetic programming, Dissolved
Oxygen, SVM, LGP, ANN, modelling",
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ISSN = "1674-9871",
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DOI = "doi:10.1016/j.gsf.2016.04.007",
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URL = "http://www.sciencedirect.com/science/article/pii/S1674987116300469",
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abstract = "Most of the water quality models previously developed
and used in dissolved oxygen (DO) prediction are
complex. Moreover, reliable data available to
develop/calibrate new DO models is scarce. Therefore,
there is a need to study and develop models that can
handle easily measurable parameters of a particular
site, even with short length. In recent decades,
computational intelligence techniques, as effective
approaches for predicting complicated and significant
indicator of the state of aquatic ecosystems such as
DO, have created a great change in predictions. In this
study, three different AI methods comprising: (1) two
types of artificial neural networks (ANN) namely multi
linear perceptron (MLP) and radial based function
(RBF); (2) an advancement of genetic programming namely
linear genetic programming (LGP); and (3) a support
vector machine (SVM) technique were used for DO
prediction in Delaware River located at Trenton, USA.
For evaluating the performance of the proposed models,
root mean square error (RMSE), Nash-Sutcliffe
efficiency coefficient (NS), mean absolute relative
error (MARE) and, correlation coefficient statistics
(R) were used to choose the best predictive model. The
comparison of estimation accuracies of various
intelligence models illustrated that the SVM was able
to develop the most accurate model in DO estimation in
comparison to other models. Also, it was found that the
LGP model performs better than the both ANNs models.
For example, the determination coefficient was 0.99 for
the best SVM model, while it was 0.96, 0.91 and 0.81
for the best LGP, MLP and RBF models, respectively. In
general, the results indicated that an SVM model could
be employed satisfactorily in DO estimation.",
- }
Genetic Programming entries for
Ehsan Olyaie
Hamid Zare Abyaneh
Ali Danandeh Mehr
Citations