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How Data Improves Leak Detection in Water Distribution Systems

Leaks in the water distribution system are costing your municipality, and your rate payers, money. Big money.

Worldwide, it is estimated that non-revenue water (NRW) accounts for 30% of distribution. Developed countries, such as Canada, fare better. Yet with the high cost of water treatment and distribution, an NRW loss of even 10% to 15% can be significant.

With most distribution infrastructure hidden underground or behind walls, detecting leaks is a huge challenge. The longer leaks are left undetected, the greater the water loss – and higher the potential for damage.

The more data you can collect on your water distribution and consumption, the better equipped you are to locate and triage sources of NRW in a timely manner. And the quicker you have this information at your fingertips – the better. 

To demonstrate how data improves leak detection , we will look at three common scenarios: a non-metered network, a metered network (including AMR meters), and a network that employs AMI (advanced metering infrastructure).

Three scenarios:

Scenario 1: Unmetered Network 

Municipalities with non-metered networks are at a distinct disadvantage when it comes to detecting leaks. Without accurate consumption monitoring, there is limited data available to calculate the amount of water loss across the system.

And while leak detection tools, such as acoustic sensors, can be placed along the network – knowing where to look involves guesswork and a little bit of good luck. Or, make that “bad luck” in the event of a burst pipe that is spewing water into the air or creating a pool on a busy street or sidewalk.

By the time a leak is discovered, the cost due to NRW is high, and the damage to homes, businesses and municipal infrastructure can be significant. Plus, moving the sensors and manually collecting data is labour intensive and costly without the ability to piggyback on an automated metering system.

Scenario 2: Metered Network 

Municipalities with water metering have a significant edge over those that do not. Meters provide an accurate picture of the amount of water that has been distributed and the amount of water consumed. This allows municipalities to run the numbers to deduce if there is a leak, and calculate the severity of NRW loss.

Through metering, it is often easy to identify if there is a leak within a home or business by measuring consumption. Higher-than-average usage or particular patterns can often be attributed to a leak.

Distribution networks that feature automated meter reading (AMR) systems have an additional advantage.  

AMR meters collect detailed consumption data between readings – with the ability to identify daily – even hourly – consumption. Data from meters is typically collected monthly or quarterly) using a walk-up or drive-by reading. In addition, acoustic leak sensors located along the network can provide specialized leak detection information, which is then collected during scheduled meter readings.

Once this data is uploaded to the central system, software can analyze and interpret the information in order to provide even more detailed breakdowns around consumption. This data makes it possible to narrow down the source (or sources) of the leak.

Meters can also have alarm functions, designed to identify leaks in a home or business. If there is a constant, prolonged flow of water, the alarm is triggered. Alarm notifications will be discovered once the data is uploaded to the system by a meter reader.

A new evolution in metering hardware is meters with acoustic leak detection sensors built-in. This would expand data collection points for leak detection, and dramatically enhance the ability to identify leaks in not only homes and businesses – but also along the distribution network.

Scenario 3: Advanced Metering Infrastructure (AMI)

Practical real-time reporting.

Having an abundance of useful data is valuable. Being able to access and interpret it in close to real-time? Priceless!

This is where advanced metering infrastructure (AMI) stands head and shoulders above the rest.

AMI constantly collects and transmits consumption data, alarms and leak sensor data to the central software platform. As it is received, software with advanced algorithms generate a comprehensive array of reports and insights to inform the decision-making process. 

As a result, municipalities can get reports on their NRW position daily. Daily! The wealth of data available will identify leaks across the system as they develop and grow – so waterworks staff can prioritize repairs and rehabilitation. If there is a leak in a home or business, the resident or landlord can be notified immediately so they can address the problem before the costs escalate.  

Leak detection is not a one-size fits all solution.

As you can see, there are numerous options for leak detection in water distribution systems, and this list is far from comprehensive.

For example, data collected through satellite imaging technology is now being used to locate leaks in municipal water distribution systems. Aerial imaging is used to detect minute differences in spectral signatures in the distribution system. One significant advantage is that the municipality’s entire distribution infrastructure can be captured and later analyzed in one satellite flyover.

At the end of the day, detecting leaks comes down to your ability to collect data that can guide your decisions. The more timely your access to that information, the more quickly you can respond.

When considering which approach will bring the greatest value to your distribution network, it is important to look at the big picture. For example, how does the detection system align with your overall metering, customer service, billing and water distribution planning strategy? What is the upfront and long-term investment cost to ratepayers? And perhaps most importantly, what are the long-term savings by reducing NRW?


Metercor provides a comprehensive offering of leak detection solutions tailored to your distribution needs. Serving Western Canada and Ontario.

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