Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology

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  author =       "A. Elshorbagy and G. Corzo and S. Srinivasulu and 
                 D. P. Solomatine",
  title =        "Experimental investigation of the predictive
                 capabilities of data driven modeling techniques in
                 hydrology - Part 1: Concepts and methodology",
  journal =      "Hydrology and Earth System Sciences",
  year =         "2010",
  volume =       "14",
  number =       "10",
  pages =        "1931--1941",
  month =        "14 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1471-2164",
  URL =          "http://www.hydrol-earth-syst-sci.net/14/1931/2010/hess-14-1931-2010.pdf",
  URL =          "http://www.hydrol-earth-syst-sci.net/14/1931/2010/",
  DOI =          "doi:10.5194/hess-14-1931-2010",
  publisher =    "Copernicus GmbH",
  abstract =     "A comprehensive data driven modelling experiment is
                 presented in a two-part paper. In this first part, an
                 extensive data-driven modeling experiment is proposed.
                 The most important concerns regarding the way data
                 driven modelling (DDM) techniques and data were
                 handled, compared, and evaluated, and the basis on
                 which findings and conclusions were drawn are
                 discussed. A concise review of key articles that
                 presented comparisons among various DDM techniques is
                 presented. Six DDM techniques, namely, neural networks,
                 genetic programming, evolutionary polynomial
                 regression, support vector machines, M5 model trees,
                 and K-nearest neighbours are proposed and explained.
                 Multiple linear regression and na{\"i}ve models are
                 also suggested as baseline for comparison with the
                 various techniques. Five datasets from Canada and
                 Europe representing evapotranspiration, upper and lower
                 layer soil moisture content, and rainfall-runoff
                 process are described and proposed, in the second
                 paper, for the modelling experiment. Twelve different
                 realisations (groups) from each dataset are created by
                 a procedure involving random sampling. Each group
                 contains three subsets; training, cross-validation, and
                 testing. Each modelling technique is proposed to be
                 applied to each of the 12 groups of each dataset. This
                 way, both prediction accuracy and uncertainty of the
                 modelling techniques can be evaluated. The description
                 of the data sets, the implementation of the modeling
                 techniques, results and analysis, and the findings of
                 the modelling experiment are deferred to the second
                 part of this paper.",
  notes =        "See also \cite{Elshorbagy:2010a:HESS} Published in
                 Hydrol. Earth Syst. Sci. Discuss.: 19 November 2009

                 1 Centre for Advanced Numerical Simulation (CANSIM),
                 Department of Civil and Geological Engineering,
                 University of Saskatchewan, Saskatoon, SK, S7N 5A9,
                 Canada 2 Department of Hydroinformatics and Knowledge
                 Management, UNESCO-IHE Institute for Water Education,
                 Delft, The Netherlands 3 Water Resources Section, Delft
                 University of Technology, Delft, The Netherlands",

Genetic Programming entries for Amin Elshorbagy G Corzo S Srinivasulu Dimitri P Solomatine