Asset deterioration analysis using multi-utility data and multi-objective data mining

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "D. A. Savic and O. Giustolisi and D. Laucelli",
  title =        "Asset deterioration analysis using multi-utility data
                 and multi-objective data mining",
  journal =      "Journal of Hydroinformatics",
  year =         "2009",
  volume =       "11",
  number =       "3-4",
  pages =        "211--224",
  keywords =     "genetic algorithms, genetic programming, EPR, asset
                 deterioration, data mining, evolutionary computing,
                 sewer, water supply networks",
  ISSN =         "1464-7141",
  URL =          "",
  DOI =          "doi:10.2166/hydro.2009.019",
  size =         "14 pages",
  abstract =     "Physically-based models derive from first principles
                 (e.g. physical laws) and rely on known variables and
                 parameters. Because these have physical meaning, they
                 also explain the underlying relationships of the system
                 and are usually transportable from one system to
                 another as a structural entity. They only require model
                 parameters to be updated. Data-driven or regressive
                 techniques involve data mining for modelling and one of
                 the major drawbacks of this is that the functional form
                 describing relationships between variables and the
                 numerical parameters is not transportable to other
                 physical systems as is the case with their classical
                 physically-based counterparts. Aimed at striking a
                 balance, Evolutionary Polynomial Regression (EPR)
                 offers a way to model multi-utility data of asset
                 deterioration in order to render model structures
                 transportable across physical systems. EPR is a
                 recently developed hybrid regression method providing
                 symbolic expressions for models and works with formulae
                 based on pseudo-polynomial expressions, usually in a
                 multi-objective scenario where the best Pareto optimal
                 models (parsimony versus accuracy) are selected from
                 data in a single case study. This article discusses the
                 improvement of EPR in dealing with multi-utility data
                 (multi-case study) where it has been tried to achieve a
                 general model structure for asset deterioration
                 prediction across different water systems.",

Genetic Programming entries for Dragan Savic Orazio Giustolisi Daniele B Laucelli