Predictive analytics for water main breaks using spatiotemporal data
Author:
Affiliation:
1. Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA, USA
2. Data Sceince and Analytics Institute, University of Oklahoma, Norman, OK, USA
Funder
US National Science Foundation
Publisher
Informa UK Limited
Subject
Water Science and Technology,Geography, Planning and Development
Link
https://www.tandfonline.com/doi/pdf/10.1080/1573062X.2021.1893363
Reference41 articles.
1. Risk assessment and risk management: Review of recent advances on their foundation
2. Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks
3. Statistical Modeling in Absence of System Specific Data: Exploratory Empirical Analysis for Prediction of Water Main Breaks
4. Chen, T. Y. J., J. A. Beekman, and S. D. Guikema. 2017. “Drinking Water Distribution Systems Asset Management: Statistical Modelling of Pipe Breaks.” In In Proc., Sessions of the Pipelines 2017: Conf. on Condition Assessment, Surveying, and Geomatics. Reston, VA: ASCE.
5. Prediction of water main failures with the spatial clustering of breaks
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