AI Technology Could Help Cities Detect Expensive Water Leaks

AI Technology Could Help Cities Detect Expensive Water Leaks

Interested in Location/Detection?


Location/Detection + Get Alerts


Developed by researchers at the University of Waterloo in collaboration with industry partners, the technology has the potential to detect even small leaks in pipes. It combines sophisticated signal processing techniques and AI software to identify telltale signs of leaks carried via sound in water pipes.

The acoustic signatures are recorded by hydrophone sensors that can be easily and inexpensively installed in existing fire hydrants without excavation or taking them out of service.

“This would allow cities to use their resources for maintenance and repairs much more effectively,” says lead researcher Roya Cody, a civil engineering Ph.D. candidate at Waterloo. “They could be more proactive as opposed to reactive.”

Municipal water systems in Canada lose an average of over 13 percent of their clean water between treatment and delivery due to leaks, bursts and other issues, according to the researchers. Countries with older infrastructure have even higher loss rates.


In addition to the economic costs of wasting treated water, chronic leaks can create health hazards, do damage to the foundations of structures and deteriorate over time, says Cody. “By catching small leaks early, we can prevent costly, destructive bursts later on.”

Researchers are now doing field tests with the hydrant sensors after reliably detecting leaks as small as 4.5 gpm in the lab.

They are also working on ways to pinpoint the location of leaks, which would allow municipalities to identify, prioritize and carry out repairs.

“Right now they react to situations by sending workers out when there is flooding or to inspect a particular pipe if it’s due to be checked because of its age,” Cody says.

The sensor technology works by preprocessing acoustic data using advanced signal processing techniques to highlight components associated with leaks. That makes it possible for machine learning algorithms to identify leaks by distinguishing their signs from the many other sources of noise in a water distribution system.

For more information, see the study’s abstract here.

