Forecasting daily lake levels using artificial intelligence approaches

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  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 =          "",
  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