Forecasting daily lake levels using artificial intelligence approaches

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@Article{Kisi2012169,

• author = "Ozgur Kisi and Jalal Shiri and Bagher Nikoofar",
• title = "Forecasting daily lake levels using artificial intelligence approaches",
• journal = "Computer \& Geosciences",
• volume = "41",
• pages = "169--180",
• year = "2012",
• ISSN = "0098-3004",
• DOI = "doi:10.1016/j.cageo.2011.08.027",
• URL = "http://www.sciencedirect.com/science/article/pii/S0098300411002950",
• keywords = "genetic algorithms, genetic programming, Lake level, Neuro-fuzzy, Neural networks, Forecast",
• 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

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