Application of Genetic Programming Models Incorporated in Optimization Models for Contaminated Groundwater Systems Management

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Datta:2014:EVOLVE,
  author =       "Bithin Datta and Om Prakash and 
                 Janardhanan Sreekanth",
  title =        "Application of Genetic Programming Models Incorporated
                 in Optimization Models for Contaminated Groundwater
                 Systems Management",
  booktitle =    "EVOLVE - A Bridge between Probability, Set Oriented
                 Numerics, and Evolutionary Computation V",
  year =         "2014",
  editor =       "Alexandru-Adrian Tantar and Emilia Tantar and 
                 Jian-Qiao Sun and Wei Zhang and Qian Ding and 
                 Oliver Schuetze and Michael Emmerich and Pierrick Legrand and 
                 Pierre {Del Moral} and Carlos A. {Coello Coello}",
  volume =       "288",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "183--199",
  address =      "Peking",
  month =        "1-4 " # jul,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Optimal
                 Monitoring Network, Groundwater Pollution,
                 Multi-Objective Optimisation, Pollution Source
                 Identification, Simulated Annealing, Impact Factors,
                 Ensemble Surrogates",
  isbn13 =       "978-3-319-07493-1",
  DOI =          "doi:10.1007/978-3-319-07494-8_13",
  abstract =     "Two different applications of Genetic Programming (GP)
                 for solving large scale groundwater management problems
                 are presented here. Efficient groundwater contamination
                 management needs solution of large sale simulation
                 models as well as solution of complex optimal decision
                 models. Often the best approach is to use linked
                 simulation optimisation models. However, the
                 integration of optimisation algorithm with large scale
                 simulation of the physical processes, which require
                 very large number of iterations, impose enormous
                 computational burden. Often typical solutions need
                 weeks of computer time. Suitably trained GP based
                 surrogate models approximating the physical processes
                 can improve the computational efficiency enormously,
                 also ensuring reasonably accurate solutions. Also, the
                 impact factors obtained from the GP models can help in
                 the design of monitoring networks under uncertainties.
                 Applications of GP for obtaining impact factors
                 implicitly based on a surrogate GP model, showing the
                 importance of a chosen monitoring location relative to
                 a potential contaminant source is also presented. The
                 first application uses GP models based impact factors
                 for optimal design of monitoring networks for efficient
                 identification of unknown contaminant sources. The
                 second application uses GP based ensemble surrogate
                 models within a linked simulation optimisation model
                 for optimal management of saltwater intrusion in
                 coastal aquifers.",
}

Genetic Programming entries for Bithin Datta Om Prakash Janardhanan Sreekanth

Citations