Data Mining for Management and Rehabilitation of Water Systems: The Evolutionary Polynomial Regression Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Giustolisi:2004:WM,

• author = "Orazio Giustolisi and Dragan A. Savic and Daniele Laucelli",
• title = "Data Mining for Management and Rehabilitation of Water Systems: The Evolutionary Polynomial Regression Approach",
• booktitle = "Wasserbauliche Mitteilungen (2004) Heft 27",
• year = "2004",
• pages = "285--296",
• address = "Germany",
• organisation = "Technische Universitaet Dresden, Institut fuer Wasserbau und technische Hydromechanik",
• keywords = "genetic algorithms, genetic programming, Evolutionary Polynomial Regression, EPR, Water Distribution Systems, Bust Risk Analysis, Data Mining, Modeling",
• URL = "https://hdl.handle.net/20.500.11970/103889",
• URL = "https://izw.baw.de/publikationen/dresdner-wasserbauliche-mitteilungen/0/2004_Wasserbauliche_Mitteilungen_Risiken_bei_der_Bemessung_und_Bewirtschaftung_von_Flie%c3%9fgew%c3%a4ssern_und_Stauanlagen.pdf",
• size = "11 pages",
• abstract = "Risk-based management and rehabilitation of water distribution systems requires that company asset data are collected and also that a methodology is available to efficiently extract information from data. The process of extracting useful information from data is called knowledge discovery and at its core is data mining. This automated analysis of large or complex datasets is performed to determine significant patterns among data. There are many data mining technologies (Decision Tree, Rule Induction, Statistical analysis, Artificial Neural Networks, etc.), but not all are useful for every type of problem. This paper deals with a novel data mining methodology for pipe burst analysis, which integrates numerical and symbolic regression. This new technique is named Evolutionary Polynomial Regression and uses polynomial structures whose exponents are selected by an evolutionary search, thus providing symbolic expressions. A case study from UK is presented to illustrate the application of the Evolutionary Polynomial Regression methodology to prediction of main bursts and to identification of the network features influencing them.",
• notes = "in English",

}

Genetic Programming entries for Orazio Giustolisi Dragan Savic Daniele B Laucelli

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