Application of Artificial Neural Networks (ANN) to model the failure of urban water mains

Raed Jafar, Isam Shahrour, Ilan Juran

Research output: Contribution to journalArticle

Abstract

This paper presents an application of Artificial Neural Networks (ANN) to model the failure rate and estimate the optimal replacement time for the individual pipes in an urban water distribution system. The performances of the ANN are examined using a 14-year data set collected in a city in the north of France. The first part of the paper presents the collected data. The second part describes the construction and validation of six ANN models. After a discussion of the performances of these models, they are used for the prediction of water mains failure and the determination of the benefit index, which allows optimization of investment for the rehabilitation and maintenance of urban water mains. The spatial repartition of the risk of degradation is illustrated using a geographic information system, which constitutes an effective tool for the elaboration of strategies of rehabilitation of water distribution systems.

Original languageEnglish (US)
Pages (from-to)1170-1180
Number of pages11
JournalMathematical and Computer Modelling
Volume51
Issue number9-10
DOIs
StatePublished - May 2010

Fingerprint

Water Distribution Systems
Artificial Neural Network
Water distribution systems
Rehabilitation
Neural networks
Water
Patient rehabilitation
Geographic Information Systems
Failure Rate
Neural Network Model
Geographic information systems
Replacement
Maintenance
Degradation
Pipe
Model
Optimization
Prediction
Estimate
Strategy

Keywords

  • ANN
  • Artificial Neural networks
  • Failure
  • GIS
  • Mains
  • Predictions
  • Statistics
  • Urban water

ASJC Scopus subject areas

  • Computer Science Applications
  • Modeling and Simulation

Cite this

Application of Artificial Neural Networks (ANN) to model the failure of urban water mains. / Jafar, Raed; Shahrour, Isam; Juran, Ilan.

In: Mathematical and Computer Modelling, Vol. 51, No. 9-10, 05.2010, p. 1170-1180.

Research output: Contribution to journalArticle

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