Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows

Abstract

Water lost through leakage from water distribution networks is often appreciable. As pressure increases on water resources, there is a growing emphasis for water service providers to minimize this loss. The objective of the work presented in this paper was to assess the online application and resulting benefits of an artificial intelligence system for detection of leaks/bursts at district meter area (DMA) level. An artificial neural network model, a mixture density network, was trained using a continually updated historic database that constructed a probability density model of the future flow profile. A fuzzy inference system was used for classification; it compared latest observed flow values with predicted flows over time windows such that in the event of abnormal flow conditions alerts are generated. From the probability density functions of predicted flows, the fuzzy inference system provides confidence intervals associated with each detection, these confidence values provide useful information for filtering and ranking alerts. Additionally an accurate estimate of abnormal flow magnitude is produced to further aid in ranking of alerts. A water supply system in the U.K. was used for a case study with near real-time flow data provided by general packet radio service. The online burst alert system was constructed to operate alongside an existing flat-line alarm system, and continuously analyze a set of 144 DMAs every hour. The new system identified a number of events and alerts were raised prior to their detection in the control room; either through flat-line alarms or customer contacts. Examples are given of alert correlation with burst reports and subsequent mains repairs for a 2-month trial period. Forty four percent of alerts were found to correspond to bursts confirmed by repair data or customer contacts, 32% of alerts were confirmed as unusual short-term demand from manual analysis, 9% were related to known industrial events, and only 15% were ghosts. The results indicate that the system is an effective and viable tool for online burst detection in water distribution systems with the potential to save water and improve customer service.