Data-mining approach to investigate sedimentation features in combined sewer overflows

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "M. Carbone and L. Berardi and D. Laucelli and 
                 P. Piro",
  title =        "Data-mining approach to investigate sedimentation
                 features in combined sewer overflows",
  journal =      "Journal of Hydroinformatics",
  year =         "2012",
  volume =       "14",
  number =       "3",
  pages =        "613--627",
  keywords =     "genetic algorithms, genetic programming, CSOs
                 (combined sewer overflows), data-mining techniques,
                 Evolutionary Polynomial Regression, urban drainage,
                 water pollutant",
  ISSN =         "1464-7141",
  DOI =          "doi:10.2166/hydro.2011.003",
  abstract =     "Sedimentation is the most common and effectively
                 practiced method of urban drainage control in terms of
                 operating installations and duration of service.
                 Assessing the percentage of suspended solids removed
                 after a given detention time is essential for both
                 design and management purposes. In previous
                 experimental studies by some of the authors, the
                 expression of iso-removal curves (i.e. representing the
                 water depth where a given percentage of suspended
                 solids is removed after a given detention time in a
                 sedimentation column) has been demonstrated to depend
                 on two parameters which describe particle settling
                 velocity and flocculation factor. This study proposes
                 an investigation of the influence of some hydrological
                 and pollutant aggregate information of the sampled
                 events on both parameters. The Multi-Objective
                 (EPR-MOGA) and Multi-Case Strategy (MCS-EPR) variants
                 of the Evolutionary Polynomial Regression (EPR) are
                 originally used as data-mining strategies. Results are
                 proved to be consistent with previous findings in the
                 field and some indications are drawn for relevant
                 practical applicability and future studies.",
  notes =        "IWA Publishing

                 Department of Soil Conservation, University of
                 Calabria, Italy E-mail:
                 Department of Civil and Environmental Engineering,
                 Technical University of Bari, Italy",

Genetic Programming entries for Marco Carbone Luigi Berardi Daniele B Laucelli Patrizia Piro