Water leakage poses a significant challenge to the economy, environment and water systems around the globe. That’s why leak detection technology has become a crucial aspect in infrastructure development, as they offer a proactive approach to mitigating leaks before they escalate.
But how can these technologies help us detect leaks automatically and remotely? How do these machine learning algorithms work? Let’s learn in this article!
The Core Principle of AI Machine Learning
Leak detection technologies typically utilize machine learning algorithms to profile tenant behaviour using historical data. By analyzing the volume and time of water usage during a typical weekday or weekend, the algorithm can recognize patterns and predict future consumption. Alarm thresholds are established based on past maximum consumption events, split by the day of the week and further divided by time. This allows the algorithm to accurately detect abnormal water usage patterns and trigger alerts if necessary.
But what if the machine is wrong? The ability to learn on-the-fly is another crucial aspect of machine learning algorithms too. In the event of a false alarm, the user has the option to manually override the software and maintain the water flow. Additionally, the algorithm will learn from this activity and adapt in the future.
Real-time Anomaly Detection
While it is possible to pre-program a device based on guessed thresholds, it’s unlikely to be accurate due to changes in water consumption patterns over time. To ensure ongoing efficient and effective operation of the system throughout the building’s lifetime, remote access and machine learning are necessary components.
Once trained, the machine learning algorithm continuously analyzes incoming data in real-time, comparing current meter readings with learned patterns. If the algorithm detects any deviations from expected behavior that are consistent with a leak, it raises a remote alert.
Integration with Control Systems
After a leak is detected, a first-stage alert should provide information on the volume and duration of water usage, giving an idea of how severe the leak is. In this alert, the users can act immediately to turn the water off or over-ride the event so the water does not switch off.
In the second stage alert, if there is no response from the users, the system can automatically shut the water off or adjust pressure levels to isolate the affected area.
Continuous Improvement
In the early stages, unfortunately flow-based monitoring is not enough to accurately pinpoint the exact type of damaging leak (for end-users) without realtime monitoring and human intervention. Usually we just want to know whether the leaks are drips, or more severe bursts, to decide the suitable solution. But sometimes, it’s either both or the issue stems from the fact that a destructive leak inevitably arises in an unforeseen section of the pipework, in a location that nobody has previously identified.
Still, leak prevention systems using machine learning are not static; they are constantly evolving. As more data is collected and analyzed, the algorithm becomes more sophisticated and accurate in its leak detection capabilities. This continuous improvement ensures that the system remains effective in safeguarding water resources over time.
Developing a solution to pinpoint the exact leak location and leak type has potential. For example, by including pressure data, it’s easier for us to find leaks even from long distances. This of course introduces further practical complexities, such as the need to record the exact layout of the pipeline infrastructure, utilizing technologies such as Digital Twin.
Conclusion
By harnessing the power of machine learning algorithms, leak prevention systems have become invaluable tools in water management. Their ability to detect leaks in real-time, adapt to changing conditions, and continuously improve makes them essential for ensuring the reliability and sustainability of water distribution networks. As technology continues to advance, we can expect even greater innovations in leak prevention systems, further enhancing their effectiveness in managing our precious water resources.