Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

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@Article{Elshorbagy:2010a:HESS,
  title =        "Experimental investigation of the predictive
                 capabilities of data driven modeling techniques in
                 hydrology - Part 2: Application",
  author =       "A. Elshorbagy and G. Corzo and S. Srinivasulu and 
                 D. P. Solomatine",
  journal =      "Hydrology and Earth System Sciences",
  year =         "2010",
  volume =       "14",
  number =       "10",
  pages =        "1943--1961",
  month =        "14 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "10275606",
  bibsource =    "OAI-PMH server at www.doaj.org",
  language =     "eng",
  oai =          "oai:doaj-articles:0b5621edb6cf47d7aee8cedce805592b",
  source =       "Hydrology and Earth System Sciences",
  URL =          "http://www.hydrol-earth-syst-sci.net/14/1943/2010/hess-14-1943-2010.pdf",
  size =         "19 pages",
  abstract =     "In this second part of the two-part paper, the data
                 driven modeling (DDM) experiment, presented and
                 explained in the first part, is implemented. Inputs for
                 the five case studies (half-hourly actual
                 evapotranspiration, daily peat soil moisture, daily
                 till soil moisture, and two daily rainfall-runoff
                 datasets) are identified, either based on previous
                 studies or using the mutual information content. Twelve
                 groups (realisations) were randomly generated from each
                 data set by randomly sampling without replacement from
                 the original data set. Neural networks (ANNs), genetic
                 programming (GP), evolutionary polynomial regression
                 (EPR), Support vector machines (SVM), M5 model trees
                 (M5), K-nearest neighbors (K-nn), and multiple linear
                 regression (MLR) techniques are implemented and applied
                 to each of the 12 realizations of each case study. The
                 predictive accuracy and uncertainties of the various
                 techniques are assessed using multiple average overall
                 error measures, scatter plots, frequency distribution
                 of model residuals, and the deterioration rate of
                 prediction performance during the testing phase. Gamma
                 test is used as a guide to assist in selecting the
                 appropriate modeling technique. Unlike two nonlinear
                 soil moisture case studies, the results of the
                 experiment conducted in this research study show that
                 ANNs were a sub-optimal choice for the actual
                 evapotranspiration and the two rainfall-runoff case
                 studies. GP is the most successful technique due to its
                 ability to adapt the model complexity to the model ed
                 data. EPR performance could be close to GP with
                 datasets that are more linear than nonlinear. SVM is
                 sensitive to the kernel choice and if appropriately
                 selected, the performance of SVM can improve. M5
                 performs very well with linear and semi linear data,
                 which cover wide range of hydrological situations. In
                 highly nonlinear case studies, ANNs, K-nn, and GP could
                 be more successful than other modelling techniques.
                 K-nn is also successful in linear situations, and it
                 should not be ignored as a potential modelling
                 technique for hydrological applications.",
  notes =        "See also \cite{Elshorbagy:2010:HESS}",
}

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

Citations