2024-10-21
Machine learning-based digital twin for water distribution network anomaly detection and localization
Publication
Publication
Presented at the
The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024) (July 2024), Ferrara, Italy
This paper presents the development of a Digital Twin (DTwin) to detect and localize the leaks in water distribution networks (WDNs), using single-stage and two-stage data-driven models. In the single-stage model, we test the anomalies in the dataset using Logistic Regression and Random Forest. In the two-stage model, a linear regression model predicts pressure differences between sensor pairs in the first stage. Based on this, we compute the distribution of residuals. In the second stage, changes in the residual distribution are classified using Multinomial Logistic Regression and Random Forest models to compute possible leak locations’ posterior probabilities. We have tested these models on a real-time dataset.
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| doi.org/10.3390/engproc2024069201 | |
| The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024) | |
| Organisation | Scientific Computing |
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Pandey, P., Mücke, N., Jain, S., Ramachandran, P., Bohte, S., & Oosterlee, K. (2024). Machine learning-based digital twin for water distribution network anomaly detection and localization. In Proceedings of the International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry. doi:10.3390/engproc2024069201 |
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