A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Near Real-Time Anomaly Event Localization by Pressure Drop Interpolation, Clustering, and Parallel Optimization of Hydraulic Model Calibration
https://orcid.org/0000-0002-5971-8338
https://orcid.org/0000-0001-9489-3986
https://orcid.org/0000-0002-7296-3872
Localization of anomaly events in near real-time (NRT) in water distribution networks is compelling but challenging for water utilities. This paper presents an integrated approach using both data-driven and hydraulic model–based methods to localize NRT anomaly events. Upon detecting an NRT anomaly event, the pressure drops at sensor locations are calculated, followed by estimating the pressure drops at junction nodes via an inverse-distance weighted interpolation method. Clustering is then performed based on the junction pressure drops and network topology to segregate and reduce the search areas. A genetic algorithm optimization is then performed with hydraulic model simulations to further locate the anomaly hotspots. The efficiency of the anomaly localization is enhanced by message passing interface (MPI)–based parallelization of optimization to facilitate NRT operations. The integrated method has been tested on both simulated simultaneous multiple leaks and real leakage events with field data, where the ground-truth leaks have been successfully covered in the clustered search areas. Leak hotspots are further pinpointed by hydraulic model–based parallel optimization within a distance of 300 m to the ground-truth leaks, indicating satisfactory accuracy in leak localization.
Near Real-Time Anomaly Event Localization by Pressure Drop Interpolation, Clustering, and Parallel Optimization of Hydraulic Model Calibration
https://orcid.org/0000-0002-5971-8338
https://orcid.org/0000-0001-9489-3986
https://orcid.org/0000-0002-7296-3872
Localization of anomaly events in near real-time (NRT) in water distribution networks is compelling but challenging for water utilities. This paper presents an integrated approach using both data-driven and hydraulic model–based methods to localize NRT anomaly events. Upon detecting an NRT anomaly event, the pressure drops at sensor locations are calculated, followed by estimating the pressure drops at junction nodes via an inverse-distance weighted interpolation method. Clustering is then performed based on the junction pressure drops and network topology to segregate and reduce the search areas. A genetic algorithm optimization is then performed with hydraulic model simulations to further locate the anomaly hotspots. The efficiency of the anomaly localization is enhanced by message passing interface (MPI)–based parallelization of optimization to facilitate NRT operations. The integrated method has been tested on both simulated simultaneous multiple leaks and real leakage events with field data, where the ground-truth leaks have been successfully covered in the clustered search areas. Leak hotspots are further pinpointed by hydraulic model–based parallel optimization within a distance of 300 m to the ground-truth leaks, indicating satisfactory accuracy in leak localization.
Near Real-Time Anomaly Event Localization by Pressure Drop Interpolation, Clustering, and Parallel Optimization of Hydraulic Model Calibration
https://orcid.org/0000-0002-5971-8338
https://orcid.org/0000-0001-9489-3986
https://orcid.org/0000-0002-7296-3872
Localization of anomaly events in near real-time (NRT) in water distribution networks is compelling but challenging for water utilities. This paper presents an integrated approach using both data-driven and hydraulic model–based methods to localize NRT anomaly events. Upon detecting an NRT anomaly event, the pressure drops at sensor locations are calculated, followed by estimating the pressure drops at junction nodes via an inverse-distance weighted interpolation method. Clustering is then performed based on the junction pressure drops and network topology to segregate and reduce the search areas. A genetic algorithm optimization is then performed with hydraulic model simulations to further locate the anomaly hotspots. The efficiency of the anomaly localization is enhanced by message passing interface (MPI)–based parallelization of optimization to facilitate NRT operations. The integrated method has been tested on both simulated simultaneous multiple leaks and real leakage events with field data, where the ground-truth leaks have been successfully covered in the clustered search areas. Leak hotspots are further pinpointed by hydraulic model–based parallel optimization within a distance of 300 m to the ground-truth leaks, indicating satisfactory accuracy in leak localization.
Near Real-Time Anomaly Event Localization by Pressure Drop Interpolation, Clustering, and Parallel Optimization of Hydraulic Model Calibration
J. Water Resour. Plann. Manage.
Zhang, Ashley Hui (author) / Cao, Fred (author) / Chew, Alvin Wei Ze (author) / Wu, Zheng Yi (author) / Kalfarisi, Rony (author) / Meng, Xue (author) / Pok, Jocelyn (author) / Wong, Juen Ming (author) / Yang, Clara (author)
2025-01-01
Article (Journal)
Electronic Resource
English
Volume ratio and pressure drop on hydraulic dynamic pressure calibration system
| British Library Online Contents | 2017
Model calibration for pressure drop in a pulse-jet cleaned fabric filter
| Elsevier | 1982
Hydraulic Network Calibration Using Genetic Optimization
| British Library Online Contents | 2002