Real-time Burst Detection in Water Distribution Systems Using a Bayesian Demand Forecasting Methodology

The negative consequences of non-revenue water losses from Water Distribution Systems (WDS) can be reduced through the successful and prompt identification of bursts and abnormal conditions. Here we present a preliminary investigation into the application of a probabilistic demand forecasting approa...

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Bibliographic Details
Main Authors: Hutton, Christopher, Kapelan, Zoran
Format: Conference Proceeding
Language:English
Subjects:
Anomaly Detection
Bayesian Statistics
Demand Forecast
Detection
Pipe Burst
Probability
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Summary:The negative consequences of non-revenue water losses from Water Distribution Systems (WDS) can be reduced through the successful and prompt identification of bursts and abnormal conditions. Here we present a preliminary investigation into the application of a probabilistic demand forecasting approach to identify pipe bursts. The method produces a probabilistic forecast of future demand under normal conditions. This, in turn, quantifies the probability that a future observation is abnormal. The method, when tested using synthetic bursts applied to a demand time-series for a UK WDS, performed well in detecting bursts, particularly those >5% of mean daily flow at night time.
ISSN:1877-7058
1877-7058
DOI:10.1016/j.proeng.2015.08.847