Statistical dependence of pipe breaks on explanatory variables

Gómez Martínez, Patricia; Cubillo, Francisco; Martín Carrasco, Francisco Javier y Garrote de Marcos, Luis (2017). Statistical dependence of pipe breaks on explanatory variables. "Water", v. 9 (n. 3); pp. 1-24. ISSN 2073-4441. https://doi.org/10.3390/w9030158.

Descripción

Título: Statistical dependence of pipe breaks on explanatory variables
Autor/es:
  • Gómez Martínez, Patricia
  • Cubillo, Francisco
  • Martín Carrasco, Francisco Javier
  • Garrote de Marcos, Luis
Tipo de Documento: Artículo
Título de Revista/Publicación: Water
Fecha: Marzo 2017
Volumen: 9
Materias:
Palabras Clave Informales: pipe breaks; explanatory variables; predictive models; statistical dependence; distribution lines; trunk mains; water supply
Escuela: E.T.S.I. Caminos, Canales y Puertos (UPM)
Departamento: Ingeniería Civil: Hidráulica y Ordenación Del Territorio
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 46389
Identificador DC: http://oa.upm.es/46389/
Identificador OAI: oai:oa.upm.es:46389
Identificador DOI: 10.3390/w9030158
URL Oficial: http://www.mdpi.com/2073-4441/9/3/158
Depositado por: Memoria Investigacion
Depositado el: 06 Jun 2017 13:00
Ultima Modificación: 06 Jun 2017 13:00
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