Real-time burst detection in Water Distribution Systems using a Bayesian demand forecasting methodology
© 2015 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
© 2015 Published by Elsevier Ltd. 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.
The data used in the paper have been collected as part of the Neptune project funded by the UK Engineering and Physical Sciences Research Council (EP/E0003192/1) and provided by Mr Ridwan Patel from Yorkshire Water services which is gratefully acknowledged.
Computing and Control for the Water Industry (CCWI2015): Sharing the best practice in water management
Vol. 119, Issue 1, pp. 13 - 18