This informal CPD article, ‘Leveraging Algorithms in Leak Prevention Systems’, was provided by Quensus, an organization with a mission to provide a more sustainable future for our clean water supply. They bring user-friendly, professional water management solutions to customers in enterprise corporations, small businesses, and the public sector.
Water leaks, a notorious culprit of extensive property damage, result in hefty repair costs for landlords, tenants, and property administrators alike. Recognising this, the importance of integrated leak monitoring systems has gained traction over recent years. A significant leap in technology has enabled these systems to identify leaks both automatically and from remote locations, primarily using machine learning algorithms. Let's delve into how these sophisticated algorithms operate and the advantages they offer.
Harnessing Machine Learning to Pinpoint Leaks
Central to modern leak detection systems is the utilisation of machine learning algorithms. These algorithms sift through historical water usage data, creating a profile for tenant behavior. By assessing the volume and frequency of water consumed during regular weekdays or weekends, the algorithm can anticipate common usage patterns. With this reservoir of information, it sets up benchmarks grounded on previous peak consumption instances. By distinguishing water usage patterns based on the day and time, these algorithms can proficiently spot irregular water consumption trends, subsequently raising an alert.
One of the standout features of machine learning is its dynamicism. In situations where an alert is deemed inaccurate, users can intervene manually, instructing the system to maintain the running of water flow. This user intervention helps the system adapt its future responses.
Is the Incorporation of Machine Learning required?
One might argue for a system set up with preset thresholds, but such an approach falters over time, given the changing patterns of water consumption. A consistently reliable operation demands the integration of machine learning coupled with remote access throughout the system's lifecycle. Lacking online connectivity, the advantages of instant notifications, remote valve operation, or the facility to grant access permissions become moot. In addition, the algorithm's continual evaluation of a building's performance ensures that every constituent of the system remains synchronised and active.
Post Leak Detection Protocol
Upon detecting a potential leak, an initial alert detailing the volume and duration of water consumption is relayed, giving insights into the severity of the leak. At this stage, end users can swiftly intervene, either halting the water flow or instructing the system to continue, offering the algorithm invaluable feedback.
Should there be no user intervention, a secondary alert is dispatched, prompting the automatic pause of water supply. To ascertain the leak's exact origin, users can activate real-time flow rate monitoring once water supply is resumed through the application. The primary objective here is distinguishing between minor drips, which can be temporarily overlooked, and major breaches demanding immediate attention. Identifying the leak becomes more streamlined by isolating particular branches or individual supply points.
Challenges and The Way Ahead
While flow-centric monitoring provides substantial insights, the precise origin of a detrimental leak remains elusive without active real-time monitoring and human intervention. Machine learning can differentiate between events, but cannot predict the exact location of leaks within the pipeline.
Future prospects include refining the technology to precisely locate leaks by incorporating pressure metrics to discern a leak's proximity. However, practical challenges arise, like meticulously documenting the intricate plumbing infrastructure, which might prove impractical. A more pragmatic approach, in our opinion, is the utilisation of tactile water sensors. Positioned in vulnerable zones, these sensors can detect and locate moisture very efficiently.
With the advent of machine learning algorithms, leak detection systems have been revolutionised. They ensure meticulous water consumption tracking, pre-empting potential leaks and averting extensive damages. The synergy of remote operability and machine learning is pivotal for the sustained efficacy of these systems. As technological horizons expand, we anticipate even more refined algorithms, amplifying the efficacy of leak monitoring and infrastructural maintenance.
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