Statistical dependence of pipe breaks on explanatory variables

Gómez Martínez, Patricia and Cubillo, Francisco and Martín Carrasco, Francisco Javier and 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.

Description

Title: Statistical dependence of pipe breaks on explanatory variables
Author/s:
  • Gómez Martínez, Patricia
  • Cubillo, Francisco
  • Martín Carrasco, Francisco Javier
  • Garrote de Marcos, Luis
Item Type: Article
Título de Revista/Publicación: Water
Date: March 2017
ISSN: 2073-4441
Volume: 9
Subjects:
Freetext Keywords: pipe breaks; explanatory variables; predictive models; statistical dependence; distribution lines; trunk mains; water supply
Faculty: E.T.S.I. Caminos, Canales y Puertos (UPM)
Department: Ingeniería Civil: Hidráulica y Ordenación Del Territorio
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (2MB) | Preview

Abstract

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.

More information

Item ID: 46389
DC Identifier: http://oa.upm.es/46389/
OAI Identifier: oai:oa.upm.es:46389
DOI: 10.3390/w9030158
Official URL: http://www.mdpi.com/2073-4441/9/3/158
Deposited by: Memoria Investigacion
Deposited on: 06 Jun 2017 13:00
Last Modified: 06 Jun 2017 13:00
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM