Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content

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@Article{Elshorbagy:2009:JH,
  author =       "Amin Elshorbagy and Ibrahim El-Baroudy",
  title =        "Investigating the capabilities of evolutionary
                 data-driven techniques using the challenging estimation
                 of soil moisture content",
  journal =      "Journal of Hydroinformatics",
  year =         "2009",
  volume =       "11",
  number =       "3-4",
  pages =        "237--251",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 polynomial regression, EPR, prediction, soil moisture,
                 tool uncertainty",
  ISSN =         "1464-7141",
  URL =          "http://www.iwaponline.com/jh/011/0237/0110237.pdf",
  DOI =          "doi:10.2166/hydro.2009.032",
  size =         "15 pages",
  abstract =     "Soil moisture has a crucial role in both the global
                 energy and hydrological cycles; it affects different
                 ecosystem processes. Spatial and temporal variability
                 of soil moisture add to its complex behaviour, which
                 undermines the reliability of most current measurement
                 methods. In this paper, two promising evolutionary
                 data-driven techniques, namely (i) Evolutionary
                 Polynomial Regression and (ii) Genetic Programming, are
                 challenged with modelling the soil moisture response to
                 the near surface atmospheric conditions. The utility of
                 the proposed models is demonstrated through the
                 prediction of the soil moisture response of three
                 experimental soil covers, used for the restoration of
                 watersheds that were disturbed by the mining industry.
                 The results showed that the storage effect of the soil
                 moisture response is the major challenging factor; it
                 can be quantified using cumulative inputs better than
                 time-lag inputs, which can be attributed to the effect
                 of the soil layer moisture-holding capacity. This
                 effect increases with the increase in the soil layer
                 thickness. Three different modelling tools are tested
                 to investigate the tool effect in data-driven
                 modelling. Despite the promising results with regard to
                 the prediction accuracy, the study demonstrates the
                 need for adopting multiple data-driven modelling
                 techniques and tools (modelling environments) to obtain
                 reliable predictions.",
  notes =        "Laucelli EPR toolbox, South Bison Hill, oil sands
                 reclamation, 1 foot or more peat layer, AB Canada,
                 Discipulus \cite{francone:manual}

                 p242 'Discipulus produced better models than EPR'. p246
                 EPR provides insight. p258 GPLAB always evolved
                 constants (not formulae).",
}

Genetic Programming entries for Amin Elshorbagy Ibrahim El-Baroudy

Citations