Convolutional Neural Network for Burst Detection in Smart Water Distribution Systems

This study examines the benefits and limitations of a convolutional neural network (CNN) burst detection model that accounts for spatially distributed information of pressure responses in a water distribution system (WDS), i.e., the differences between measured and predicted pressure data. To that e...

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Bibliographic Details
Published in:Water resources management 2023-07, Vol.37 (9), p.3729-3743
Main Authors: Jun, Sanghoon, Lansey, Kevin E.
Format: Article
Language:English
Subjects:
Advanced metering infrastructure
Artificial neural networks
Atmospheric Sciences
Civil Engineering
Data structures
Detection
Distribution
Earth and Environmental Science
Earth Sciences
Environment
Error detection
Errors
Evaluation
Geotechnical Engineering & Applied Earth Sciences
Hydraulic models
Hydraulics
Hydrogeology
Hydrology/Water Resources
Measurement
Measuring instruments
Neural networks
Pressure
Water distribution
Water distribution systems
Water engineering
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Description
Summary:This study examines the benefits and limitations of a convolutional neural network (CNN) burst detection model that accounts for spatially distributed information of pressure responses in a water distribution system (WDS), i.e., the differences between measured and predicted pressure data. To that end, a 2D CNN is applied to a smart WDS where all pressures and advanced metering infrastructure (AMI) end-user demands are measured. Here, a well-calibrated hydraulic model for a WDS in Austin, TX is analyzed with measured AMI demands to predict pressure surfaces that are provided to a CNN. Alternative image data structures are examined to evaluate their importance and two different data types, raw pressure data and pressure responses, are evaluated to investigate the benefits of linking CNN with hydraulic information. In addition, the effect of field measurement errors on detection results is examined for a range of error magnitudes. Finally, burst detection results of partial and full pressure meters are assessed to study the benefits of pressure supplemented AMI systems. Based on the numerical results, several conclusions are posed. First, network layout information should be incorporated into the image data structure. In addition, CNN should incorporate hydraulic information within AMI demands rather than using raw pressure data. Lastly, large measurement errors can mask the impact of small bursts and SCADA systems are insufficient to detect these failures. Thus, pressure supplemented AMI systems are recommended.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-023-03524-x