A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm

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@Article{Kisi:2015:AMC,
  author =       "Ozgur Kisi and Jalal Shiri and Sepideh Karimi and 
                 Shahaboddin Shamshirband and Shervin Motamedi and 
                 Dalibor Petkovic and Roslan Hashim",
  title =        "A survey of water level fluctuation predicting in
                 Urmia Lake using support vector machine with firefly
                 algorithm",
  journal =      "Applied Mathematics and Computation",
  volume =       "270",
  pages =        "731--743",
  year =         "2015",
  ISSN =         "0096-3003",
  DOI =          "doi:10.1016/j.amc.2015.08.085",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0096300315011418",
  abstract =     "Forecasting lake level at various horizons is a
                 critical issue in navigation, water resource planning
                 and catchment management. In this article, multistep
                 ahead predictive models of predicting daily lake levels
                 for three prediction horizons were created. The models
                 were developed using a novel method based on support
                 vector machine (SVM) coupled with firefly algorithm
                 (FA). The FA was applied to estimate the optimal SVM
                 parameters. Daily water-level data from Urmia Lake in
                 north western Iran were used to train, test and
                 validate the used technique. The prediction results of
                 the SVM-FA models were compared to the genetic
                 programming (GP) and artificial neural networks (ANNs)
                 models. The experimental results showed that an
                 improvement in the predictive accuracy and capability
                 of generalization can be achieved by the SVM-FA
                 approach in comparison to the GP and ANN in 1 day ahead
                 lake level forecast. Moreover, the findings indicated
                 that the developed SVM-FA models can be used with
                 confidence for further work on formulating a novel
                 model of predictive strategy for lake level
                 prediction.",
  keywords =     "genetic algorithms, genetic programming, Urmia Lake,
                 Time series, Prediction, Support vector machine,
                 Firefly algorithm",
}

Genetic Programming entries for Ozgur Kisi Jalal Shiri Sepideh Karimi Shahaboddin Shamshirband Shervin Motamedi Dalibor Petkovic Roslan Hashim

Citations