Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique

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@Article{DanandehMehr:2013:JH,
  author =       "Ali Danandeh Mehr and Ercan Kahya and Ehsan Olyaie",
  title =        "Streamflow prediction using linear genetic programming
                 in comparison with a neuro-wavelet technique",
  journal =      "Journal of Hydrology",
  volume =       "505",
  pages =        "240--249",
  year =         "2013",
  keywords =     "genetic algorithms, genetic programming, Feed forward
                 neural networks, Wavelet transform, Data
                 pre-processing, Hydrologic models, Stream-flow
                 prediction",
  ISSN =         "0022-1694",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0022169413007105",
  DOI =          "doi:10.1016/j.jhydrol.2013.10.003",
  abstract =     "Accurate prediction of stream flow is an essential
                 ingredient for both water quantity and quality
                 management. In recent years, artificial intelligence
                 (AI) techniques have been pronounced as a branch of
                 computer science to model wide range of hydrological
                 processes. A number of research works have been still
                 comparing these techniques in order to find more
                 efficient approach in terms of accuracy and
                 applicability. In this study, two AI techniques,
                 including hybrid wavelet-artificial neural network
                 (WANN) and linear genetic programming (LGP) technique
                 have been proposed to forecast monthly stream-flow in a
                 particular catchment and then performance of the
                 proposed models were compared with each other in terms
                 of root mean square error (RMSE) and Nash-Sutcliffe
                 efficiency (NSE) measures. In this way, six different
                 monthly streamflow scenarios based on records of two
                 successive gauging stations have been modelled by a
                 common three layer artificial neural network (ANN)
                 method as the primary reference models. Then main time
                 series of input(s) and output records were decomposed
                 into sub-time series components using wavelet
                 transform. In the next step, sub-time series of each
                 model were imposed to ANN to develop WANN models as
                 optimized version of the reference ANN models. The
                 obtained results were compared with those that have
                 been developed by LGP models. Our results showed the
                 higher performance of LGP over WANN in all reference
                 models. An explicit LGP model constructed by only basic
                 arithmetic functions including one month-lagged records
                 of both target and upstream stations revealed the best
                 prediction model for the study catchment.",
  notes =        "LGP is found to be more applicable than WANN for
                 monthly streamflow prediction at Coruh River.",
}

Genetic Programming entries for Ali Danandeh Mehr Ercan Kahya Ehsan Olyaie

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