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Failure Prediction of Municipal Water Pipes Using Machine Learning Algorithms

Author

Listed:
  • Wei Liu

    (Tongji University
    Tongji University)

  • Binhao Wang

    (Tongji University)

  • Zhaoyang Song

    (Shanghai National Engineering Research Center of Urban Water Resources Co., Ltd)

Abstract

Pipe failure prediction has become a crucial demand of operators in daily operation and asset management due to the increase in operation risks of water distribution networks. In this paper, two machine learning algorithms, namely, random forest (RF) and logistic regression (LR) algorithms are employed for pipe failure prediction. RF algorithm consists of a group of decision trees that predicts pipe failure independently and makes the final decision by voting together. For the LR algorithm, the mapping relationship between existing data and decision variables is expressed by the logistic function. Then, the prediction is made by comparing the conditional probability with the fixed threshold value. The proposed algorithms are illustrated using an actual water distribution network in China. Results indicate that the RF algorithm performs better than the LR algorithm in terms of accuracy, recall, and area under the receiver operating characteristic curve. The effects of seven characteristics on pipe failures are analyzed, and diameter and length are identified as the top two influential factors. Graphical Abstract

Suggested Citation

  • Wei Liu & Binhao Wang & Zhaoyang Song, 2022. "Failure Prediction of Municipal Water Pipes Using Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1271-1285, March.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:4:d:10.1007_s11269-022-03080-w
    DOI: 10.1007/s11269-022-03080-w
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    References listed on IDEAS

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    1. Robles-Velasco, Alicia & Cortés, Pablo & Muñuzuri, Jesús & Onieva, Luis, 2020. "Prediction of pipe failures in water supply networks using logistic regression and support vector classification," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    2. Debón, A. & Carrión, A. & Cabrera, E. & Solano, H., 2010. "Comparing risk of failure models in water supply networks using ROC curves," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 43-48.
    3. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
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    5. Khalil Ardeshirtanha & Ahmad Sharafati, 2020. "Assessment of Water Supply Dam Failure Risk: Development of New Stochastic Failure Modes and Effects Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1827-1841, March.
    6. Mahsa Ghandi & Abbas Roozbahani, 2020. "Risk Management of Drinking Water Supply in Critical Conditions Using Fuzzy PROMETHEE V Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 595-615, January.
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    Cited by:

    1. Gal Perelman & Barak Fishbain, 2022. "Critical Elements Analysis of Water Supply Systems to Improve Energy Efficiency in Failure Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3797-3811, August.

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