Name
Abstract | ||
|---|---|---|
Undetected leaks in water distribution networks are a significant problem both economically and environmentally. Across Australia 12% of water is estimated to be lost through leaks and the annual cost to water utilities worldwide is US$14 billion. A sensor network that measures water flow in the pipes can be used to predict the location and size of leaks. Recent advances in sensor technology and lower costs mean that large scale sensor networks may soon be an economic choice for solving the leak detection problem. This paper presents a sensor network design method that generates human-readable rules for leak detection. Additionally, for a given network and range of operating scenarios, it discovers the best locations for flow sensors. The method is demonstrated to make acceptably accurate predictions under real-world conditions of uncertain measurements. It also allows trade-offs to be made between minimising the costs of installing and maintaining sensors and maximising prediction accuracy. For example, in some cases sufficiently accurate predictions can be made using sensors on only half the pipes. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ | ISSNIP |
Field | DocType | ISBN |
Water flow,Computer science,Flow (psychology),Distribution networks,Installation,Wireless sensor network,Distributed computing,Leak detection | Conference | 978-1-4799-8054-3 |
Citations | PageRank | References |
1 | 0.40 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rachel Cardell-Oliver | 1 | 271 | 33.25 |
| Verity Scott | 2 | 1 | 0.40 |
| Tom Chapman | 3 | 1 | 0.40 |
| Jon Morgan | 4 | 1 | 0.40 |
| Angus R. Simpson | 5 | 45 | 4.95 |