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Open AccessArticle

Statistical Dependence of Pipe Breaks on Explanatory Variables

Department of Civil Engineering: Hydraulics, Energy and Environment, Technical University of Madrid, C/Profesor Aranguren s/n, Madrid 28040, Spain
Canal de Isabel II Gestión S. A., Research, Development and Inovation Department, C/Santa Engracia No. 125, Madrid 28003, Spain
Author to whom correspondence should be addressed.
Academic Editor: Richard C. Smardon
Water 2017, 9(3), 158;
Received: 28 December 2016 / Revised: 16 February 2017 / Accepted: 20 February 2017 / Published: 24 February 2017
Aging infrastructure is the main challenge currently faced by water suppliers. Estimation of assets lifetime requires reliable criteria to plan assets repair and renewal strategies. To do so, pipe break prediction is one of the most important inputs. This paper analyzes the statistical dependence of pipe breaks on explanatory variables, determining their optimal combination and quantifying their influence on failure prediction accuracy. A large set of registered data from Madrid water supply network, managed by Canal de Isabel II, has been filtered, classified and studied. Several statistical Bayesian models have been built and validated from the available information with a technique that combines reference periods of time as well as geographical location. Statistical models of increasing complexity are built from zero up to five explanatory variables following two approaches: a set of independent variables or a combination of two joint variables plus an additional number of independent variables. With the aim of finding the variable combination that provides the most accurate prediction, models are compared following an objective validation procedure based on the model skill to predict the number of pipe breaks in a large set of geographical locations. As expected, model performance improves as the number of explanatory variables increases. However, the rate of improvement is not constant. Performance metrics improve significantly up to three variables, but the tendency is softened for higher order models, especially in trunk mains where performance is reduced. Slight differences are found between trunk mains and distribution lines when selecting the most influent variables and models. View Full-Text
Keywords: pipe breaks; explanatory variables; predictive models; statistical dependence; distribution lines; trunk mains; water supply pipe breaks; explanatory variables; predictive models; statistical dependence; distribution lines; trunk mains; water supply
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Gómez-Martínez, P.; Cubillo, F.; Martín-Carrasco, F.J.; Garrote, L. Statistical Dependence of Pipe Breaks on Explanatory Variables. Water 2017, 9, 158.

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