Asset deterioration analysis using multi-utility data and multi-objective data mining
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- @Article{Savic:2009:JH,
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author = "D. A. Savic and O. Giustolisi and D. Laucelli",
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title = "Asset deterioration analysis using multi-utility data
and multi-objective data mining",
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journal = "Journal of Hydroinformatics",
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year = "2009",
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volume = "11",
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number = "3-4",
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pages = "211--224",
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keywords = "genetic algorithms, genetic programming, EPR, asset
deterioration, data mining, evolutionary computing,
sewer, water supply networks",
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ISSN = "1464-7141",
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URL = "http://www.iwaponline.com/jh/011/0211/0110211.pdf",
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DOI = "doi:10.2166/hydro.2009.019",
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size = "14 pages",
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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
Citations