This paper focusses on the customisation and further enhancement of the recently developed data-driven methodology for the automated near real-time detection of pipe bursts and other (e.g. sensor faults) events at the district metered area (DMA) level. Assuming the availability of pressure/flow data from an increased number of sensors deployed in a DMA, the aim is to: (i) overcome the limitations of the probabilistic inference engine when dealing with the increased data availability; and (ii) exploit the event information resulting from the analysis of the larger number of DMA signals for determining the approximate location of the pipe burst events within the DMA. This is achieved by making use of a multivariate Gaussian mixtures-based graphical model and geostatistical techniques. The novel detection and location methodology is demonstrated and tested on a series of simulated pipe burst events that were performed by opening hydrants in a real-life DMA in the UK. The results obtained illustrate that the new methodology can successfully determine the approximate location of pipe bursts within a DMA (in addition to detecting them in a fast and reliable manner). The performance comparison of several geostatistical techniques shows that the Ordinary Cokriging technique outperforms all other techniques tested.

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