A Genetic Programming-Based Surrogate Model Development and Its Application to a Groundwater Source Identification Problem

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@InProceedings{Zechman:2005:WWREC2,
  author =       "Emily Zechman and Baha Mirghani and 
                 G. Mahinthakumar and S. Ranji Ranjithan",
  title =        "A Genetic Programming-Based Surrogate Model
                 Development and Its Application to a Groundwater Source
                 Identification Problem",
  booktitle =    "World Water and Environmental Resources Congress
                 2005",
  year =         "2005",
  editor =       "Raymond Walton",
  address =      "Anchorage, Alaska, USA",
  publisher_address = "Reston, Va, USA",
  month =        may # " 15-19",
  organisation = "American Society Civil Engineering",
  keywords =     "genetic algorithms, genetic programming, Chemicals,
                 Groundwater management, Hydrologic models, Water
                 pollution, Wells",
  isbn13 =       "978-0-7844-0792-9",
  DOI =          "doi:10.1061/40792(173)341",
  abstract =     "This paper investigates a groundwater source
                 identification problem in which chemical signals at
                 observation wells are used to reconstruct the pollution
                 loading scenario. This inverse problem is solved using
                 a simulation-optimisation approach that uses a genetic
                 algorithm to conduct the search. As the numerical
                 pollution-transport model is solved iteratively during
                 the heuristic search, the evolutionary search can be in
                 general computationally intensive. This is addressed by
                 constructing a surrogate modelling approach that is
                 able to predict quickly the concentration profiles at
                 the observation wells. A genetic program is used in the
                 development of the surrogate models that provides an
                 acceptable prediction performance. The surrogate model,
                 which replaces the numerical simulation model, is then
                 coupled with the evolutionary search procedure to solve
                 the inverse problem. The results will illustrate 1) the
                 performance of the surrogate model in predicting the
                 concentration compared with the predictions using the
                 original numerical model, and 2) the quality of the
                 solution to the inverse problem obtained using the
                 surrogate model to that obtained using the numerical
                 model.",
  notes =        "Environmental and Water Resources Institute (EWRI) of
                 ASCE.

                 OCLC Number: 66144369

                 c2005 ASCE",
}

Genetic Programming entries for Emily M Zechman Baha Y Mirghani G (Kumar) Mahinthakumar S Ranji Ranjithan

Citations