Comparison of data-driven modelling techniques for river flow forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Londhe:2010:HSJ,
  author =       "Shreenivas Londhe and Shrikant Charhate",
  title =        "Comparison of data-driven modelling techniques for
                 river flow forecasting",
  journal =      "Hydrological Sciences Journal",
  year =         "2010",
  volume =       "55",
  number =       "7",
  pages =        "1163--1174",
  keywords =     "genetic algorithms, genetic programming, streamflow,
                 data-driven modelling, artificial neural networks,
                 genetic programming, M5 model trees",
  ISSN =         "02626667",
  DOI =          "doi:10.1080/02626667.2010.512867",
  size =         "12 pages",
  abstract =     "Accurate forecasting of streamflow is essential for
                 the efficient operation of water resources systems. The
                 streamflow process is complex and highly nonlinear.
                 Therefore, researchers try to devise alternative
                 techniques to forecast streamflow with relative ease
                 and reasonable accuracy, although traditional
                 deterministic and conceptual models are available. The
                 present work uses three data-driven techniques, namely
                 artificial neural networks (ANN), genetic programming
                 (GP) and model trees (MT) to forecast river flow one
                 day in advance at two stations in the Narmada catchment
                 of India, and the results are compared. All the models
                 performed reasonably well as far as accuracy of
                 prediction is concerned. It was found that the ANN and
                 MT techniques performed almost equally well, but GP
                 performed better than both these techniques, although
                 only marginally in terms of prediction accuracy in
                 normal and extreme events.",
  notes =        "Department of Civil Engineering, Vishwakarma Institute
                 of Information Technology, Survey no. 2/3/4, Kondhwa
                 (Bk), Pune, MH, 411048, India

                 Department of Civil Engineering, Datta Meghe College of
                 Engineering, Airoli, Navi Mumbai, MH, 400708,
                 India

                 p1172 'Rajghat and Mandaleshwar in the Narmada basin in
                 India. The GP models performed better compared to ANN
                 and MT models, though marginally.'

                 Comparaison de techniques de modelisation conditionnee
                 par les donnees pour la prevision des debits fluviaux",
}

Genetic Programming entries for S N Londhe S B Charhate

Citations