Real-time Burst Detection in Water Distribution Systems Using a Bayesian Demand Forecasting Methodology. (2015)

• Record Type: Journal Article
• Authors: Hutton, Christopher; Kapelan, Zoran
• Is Part Of: Procedia engineering. Volume 119(2015)
• Page Start: 13
• Page End: 18
• Publication Date: 2015
• Subjects: Pipe Burst -- Detection -- Demand Forecast -- Bayesian Statistics -- Anomaly Detection -- Probability; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2015.08.847
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: 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.