Modelling discharge-sediment relationship using neural networks with artificial bee colony algorithm
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- @Article{Kisi201294,
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author = "Ozgur Kisi and Coskun Ozkan and Bahriye Akay",
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title = "Modelling discharge-sediment relationship using neural
networks with artificial bee colony algorithm",
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journal = "Journal of Hydrology",
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volume = "428-429",
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pages = "94--103",
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year = "2012",
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ISSN = "0022-1694",
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DOI = "doi:10.1016/j.jhydrol.2012.01.026",
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URL = "http://www.sciencedirect.com/science/article/pii/S0022169412000698",
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keywords = "genetic algorithms, genetic programming, Suspended
sediment, Modelling, Neural networks, Artificial bee
colony algorithm",
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abstract = "Estimation of suspended sediment concentration carried
by a river is very important for many water resources
projects. The accuracy of artificial neural networks
(ANN) with artificial bee colony (ABC) algorithm is
investigated in this paper for modelling
discharge-suspended sediment relationship. The ANN-ABC
was compared with those of the neural differential
evolution, adaptive neuro-fuzzy, neural networks and
rating curve models. The daily stream flow and
suspended sediment concentration data from two
stations, Rio Valenciano Station and Quebrada Blanca
Station, were used as case studies. For evaluating the
ability of the models, mean square error and
determination coefficient criteria were used.
Comparison results showed that the ANN-ABC was able to
produce better results than the neural differential
evolution, neuro-fuzzy, neural networks and rating
curve models. The logarithm transformed data were also
used as input to the proposed ANN-ABC models. It was
found that the logarithm transform significantly
increased accuracy of the models in suspended sediment
estimation.",
- }
Genetic Programming entries for
Ozgur Kisi
Coskun Ozkan
Bahriye Akay
Citations