TY - GEN
T1 - One meter to find them all-water network leak localization using a single flow meter
AU - Narayanan, Iyswarya
AU - Vasan, Arunchandar
AU - Sarangan, Venkatesh
AU - Sivasubramaniam, Anand
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - Leak localization is a major issue faced by water utilities worldwide. Leaks are ideally detected and localized by a network-wide metering infrastructure. However, in many utilities, in-network metering is minimally present at just the inlets of subnetworks called District Metering Area (DMA). We consider the problem of leak localization using data from a single flow meter placed at the inlet of a DMA. We use standard time-series based modeling to detect if a current meter reading is a leak or not, and if so, to estimate the excess flow. Conventional approaches use an a-priori fully calibrated hydraulic model to map the excess flow back to a set of candidate leak locations. However, obtaining an accurate hydraulic model is expensive and hence, beyond the reach of many water utilities. We present an alternate approach that exploits the network structure and static properties in a novel way. Specifically, we extend the use of centrality metrics to infrastructure domains and use these metrics to map from the excess leak flow to the candidate leak location(s). We evaluate our approach on benchmark water utility network topologies as well as on real data obtained from an European water utility. On benchmark topologies, the localization obtained by our method is comparable to that obtained from a complete hydraulic model. On a real-world network, we were able to localize two out of the three leaks whose data we had access to. Of these two cases, we find that the actual leak location was in the candidate set identified by our approach; further, the approach pruned as much as 78% of the DMA locations, indicating a high degree of localization.
AB - Leak localization is a major issue faced by water utilities worldwide. Leaks are ideally detected and localized by a network-wide metering infrastructure. However, in many utilities, in-network metering is minimally present at just the inlets of subnetworks called District Metering Area (DMA). We consider the problem of leak localization using data from a single flow meter placed at the inlet of a DMA. We use standard time-series based modeling to detect if a current meter reading is a leak or not, and if so, to estimate the excess flow. Conventional approaches use an a-priori fully calibrated hydraulic model to map the excess flow back to a set of candidate leak locations. However, obtaining an accurate hydraulic model is expensive and hence, beyond the reach of many water utilities. We present an alternate approach that exploits the network structure and static properties in a novel way. Specifically, we extend the use of centrality metrics to infrastructure domains and use these metrics to map from the excess leak flow to the candidate leak location(s). We evaluate our approach on benchmark water utility network topologies as well as on real data obtained from an European water utility. On benchmark topologies, the localization obtained by our method is comparable to that obtained from a complete hydraulic model. On a real-world network, we were able to localize two out of the three leaks whose data we had access to. Of these two cases, we find that the actual leak location was in the candidate set identified by our approach; further, the approach pruned as much as 78% of the DMA locations, indicating a high degree of localization.
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U2 - 10.1109/IPSN.2014.6846740
DO - 10.1109/IPSN.2014.6846740
M3 - Conference contribution
AN - SCOPUS:84904685552
SN - 9781479931460
T3 - IPSN 2014 - Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)
SP - 47
EP - 58
BT - IPSN 2014 - Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)
PB - IEEE Computer Society
T2 - 13th IEEE/ACM International Conference on Information Processing in Sensor Networks, IPSN 2014
Y2 - 15 April 2014 through 17 April 2014
ER -