Ensemble modeling approach for rainfall/groundwater balancing

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

@Article{Laucelli:2007:JH,
  author =       "D. Laucelli and O. Giustolisi and V. Babovic and 
                 M. Keijzer",
  title =        "Ensemble modeling approach for rainfall/groundwater
                 balancing",
  journal =      "Journal of Hydroinformatics",
  year =         "2007",
  volume =       "9",
  number =       "2",
  pages =        "95--106",
  publisher =    "IWA Publishing",
  keywords =     "genetic algorithms, genetic programming, ensemble
                 modelling, groundwater, hydrology",
  ISSN =         "1464-7141",
  URL =          "http://www.iwaponline.com/jh/009/0095/0090095.pdf",
  DOI =          "doi:10.2166/hydro.2007.102",
  size =         "12 pages",
  abstract =     "This paper introduces an application of machine
                 learning, on real data. It deals with Ensemble
                 Modelling, a simple averaging method for obtaining more
                 reliable approximations using symbolic regression.
                 Considerations on the contribution of bias and variance
                 to the total error, and ensemble methods to reduce
                 errors due to variance, have been tackled together with
                 a specific application of ensemble modeling to
                 hydrological forecasts. This work provides empirical
                 evidence that genetic programming can greatly benefit
                 from this approach in forecasting and simulating
                 physical phenomena. Further considerations have been
                 taken into account, such as the influence of Genetic
                 Programming parameter settings on the model's
                 performance.",
  notes =        "Piana di Brindisi",
}

Genetic Programming entries for Daniele B Laucelli Orazio Giustolisi Vladan Babovic Maarten Keijzer

Citations