Machine Learning-Based Digital Twin for Water Distribution Network Anomaly Detection and Localization †
Abstract
:1. Introduction
2. Network and Dataset
2.1. Sensors Placement
2.2. Data Collection
2.3. Leak Location
2.4. Use of Machine Learning Models
3. Results and Discussion
3.1. Results Using Single-Stage Model
3.2. Results Using Two-Stage Model
3.3. Comparison of Single-Stage and Two-Stage Models When a Limited Number of Sensors Are Utilized
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Pandey, P.; Mücke, N.T.; Jain, S.; Ramachandran, P.; Bohté, S.M.; Oosterlee, C.W. Machine Learning-Based Digital Twin for Water Distribution Network Anomaly Detection and Localization. Eng. Proc. 2024, 69, 201. https://doi.org/10.3390/engproc2024069201
Pandey P, Mücke NT, Jain S, Ramachandran P, Bohté SM, Oosterlee CW. Machine Learning-Based Digital Twin for Water Distribution Network Anomaly Detection and Localization. Engineering Proceedings. 2024; 69(1):201. https://doi.org/10.3390/engproc2024069201
Chicago/Turabian StylePandey, Prerna, Nikolaj T. Mücke, Shashi Jain, Parthasarathy Ramachandran, Sander M. Bohté, and Cornelis W. Oosterlee. 2024. "Machine Learning-Based Digital Twin for Water Distribution Network Anomaly Detection and Localization" Engineering Proceedings 69, no. 1: 201. https://doi.org/10.3390/engproc2024069201
APA StylePandey, P., Mücke, N. T., Jain, S., Ramachandran, P., Bohté, S. M., & Oosterlee, C. W. (2024). Machine Learning-Based Digital Twin for Water Distribution Network Anomaly Detection and Localization. Engineering Proceedings, 69(1), 201. https://doi.org/10.3390/engproc2024069201