Forecasting daily lake levels using artificial intelligence approaches
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- @Article{Kisi2012169,
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author = "Ozgur Kisi and Jalal Shiri and Bagher Nikoofar",
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title = "Forecasting daily lake levels using artificial
intelligence approaches",
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journal = "Computer \& Geosciences",
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volume = "41",
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pages = "169--180",
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year = "2012",
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ISSN = "0098-3004",
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DOI = "doi:10.1016/j.cageo.2011.08.027",
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URL = "http://www.sciencedirect.com/science/article/pii/S0098300411002950",
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keywords = "genetic algorithms, genetic programming, Lake level,
Neuro-fuzzy, Neural networks, Forecast",
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abstract = "Accurate prediction of lake-level variations is
important for planning, design, construction, and
operation of lake shore structures and also in the
management of freshwater lakes for water supply
purposes. In the present paper, three artificial
intelligence approaches, namely artificial neural
networks (ANNs), adaptive-neuro-fuzzy inference system
(ANFIS), and gene expression programming (GEP), were
applied to forecast daily lake-level variations up to
3-day ahead time intervals. The measurements at the
Lake Iznik in Western Turkey, for the period of January
1961-December 1982, were used for training, testing,
and validating the employed models. The results
obtained by the GEP approach indicated that it performs
better than ANFIS and ANNs in predicting lake-level
variations. A comparison was also made between these
artificial intelligence approaches and convenient
autoregressive moving average (ARMA) models, which
demonstrated the superiority of GEP, ANFIS, and ANN
models over ARMA models.",
- }
Genetic Programming entries for
Ozgur Kisi
Jalal Shiri
Bagher Nikoofar
Citations