An evolutionary multiobjective strategy for the effective management of groundwater resources

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "O. Giustolisi and A. Doglioni and D. A. Savic and 
                 F. {di Pierro}",
  title =        "An evolutionary multiobjective strategy for the
                 effective management of groundwater resources",
  journal =      "Water Resources Research",
  year =         "2008",
  volume =       "44",
  number =       "1",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, EPR,
                 Data-driven, modelling, evolutionary search,
                 multiobjective, groundwater resources, efficient
                 management, planning",
  ISSN =         "1944-7973",
  publisher =    "American Geophysical Union",
  DOI =          "doi:10.1029/2006WR005359",
  size =         "14 pages",
  abstract =     "This paper introduces a modelling approach aimed at
                 the management of groundwater resources based on a
                 hybrid multiobjective paradigm, namely Evolutionary
                 Polynomial Regression. Multiobjective modeling in
                 hybrid evolutionary computing enables the user (a) to
                 find a set of feasible symbolic models, (b) to make a
                 robust choice of models and (c) to improve
                 computational efficiency, simultaneously developing a
                 set of models with diverse structural parsimony levels.
                 Moreover, this methodology appears to be well suited to
                 those cases where process input and the boundary
                 conditions are not easily accessible. The
                 multiobjective approach is based on the Pareto
                 dominance criterion and it is fully integrated into the
                 Evolutionary Polynomial Regression paradigm. This
                 approach proves to be effective for modelling
                 groundwater systems, which usually requires (a)
                 accurate analyses of the underlying physical phenomena,
                 (b) reliable forecasts under different hypothetical
                 scenarios and (c) good generalisation features of the
                 models identified. For these reasons it is important to
                 construct easily interpretable models which are
                 specialised for well defined purposes. The proposed
                 methodology is tested on a case study aimed at
                 determining the dynamic relationship between rainfall
                 depth and water table depth for a shallow unconfined
                 aquifer located in southeast Italy.",
  notes =        "Brindisi. no page numbers, W01403, wrcr11027.pdf",

Genetic Programming entries for Orazio Giustolisi Angelo Doglioni Dragan Savic Francesco di Pierro