Developing an Open Repository of Water Main Break Prediction Models in Kitchener
Author:
Affiliation:
1. Building, Civil and Environmental Engineering Department, Concordia University, Montreal, QC H3G 2W1, Canada
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/69/1/13/pdf
Reference10 articles.
1. Evaluating risk of water mains failure using a Bayesian belief network model;Kabir;Eur. J. Oper. Res.,2015
2. Improving pipe failure predictions: Factors effecting pipe failure in drinking water networks;Barton;Water Res.,2019
3. Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors;Fan;Reliab. Eng. Syst. Saf.,2022
4. Snider, B., and Mcbean, E.A. (2018, January 23–25). Improving time-to-failure predictions for water distribution systems using gradient boosting algorithm. Proceedings of the 1st International WDSA/CCWI 2018 Joint Conference, Kingston, ON, Canada.
5. Performance evaluation of pipe break machine learning models using datasets from multiple utilities;Chen;J. Infrastruct. Syst.,2022
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