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Prediction of Timing of Watermain Failure Using Gene Expression Models

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  • Ahmed Sattar
  • B. Gharabaghi
  • Edward McBean

Abstract

An innovative predictive equation characterizing watermain failure timing developed from datasets of historical failures in Greater Toronto Area (GTA), Ontario, Canada is described. Gene expression programming (GEP) is used to develop empirical relations between the time to failure and control variables including protection methods for three types of pipes, Cast Iron, Ductile Iron, and Asbestos Cement. The developed GEP model has a correlation coefficient of 0.68, and with the advantage for predicting not only the time to first break, but also subsequent breaks. The prediction uncertainties of the developed GEP were 38 % of the median value for time to next failure. A parametric analysis is performed for further verification of the developed GEP model showing the relation is simple, yet effectively forecasts watermain timing to first failure and subsequent failures. Simulated failure scenarios indicate a return to high failure rates if cement mortar lining and cathodic protection are not extended to all candidate pipes in the distribution system. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Ahmed Sattar & B. Gharabaghi & Edward McBean, 2016. "Prediction of Timing of Watermain Failure Using Gene Expression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1635-1651, March.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:5:p:1635-1651
    DOI: 10.1007/s11269-016-1241-x
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    References listed on IDEAS

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    Cited by:

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    2. Gema Sakti Raspati & Stian Bruaset & Camillo Bosco & Lars Mushom & Birgitte Johannessen & Rita Ugarelli, 2022. "A Risk-Based Approach in Rehabilitation of Water Distribution Networks," IJERPH, MDPI, vol. 19(3), pages 1-24, January.
    3. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
    4. Shaghaghi, Saba & Bonakdari, Hossein & Gholami, Azadeh & Ebtehaj, Isa & Zeinolabedini, Maryam, 2017. "Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 271-286.
    5. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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