How Machine Learning is Revolutionizing Leak Detection in Sanitation

If you are a loss manager at a water utility, you know that identify non-visible leaks – those that have not yet reached the surface – is one of the biggest operational challenges. But now, technology makes it possible to do this proactive, automated and intelligent. That’s exactly what Machine Learning is making possible.

What is Machine Learning?

Machine learning is a branch of artificial intelligence that allows systems to learn to identify patterns in large volumes of data and make decisions based on them, without being explicitly programmed for each task. Over time, these algorithms become more accurate as they are exposed to new data, and are widely used in areas such as health, finance, transportation and, more recently, sanitation.

Artificial Intelligence and Machine Learning: what is the relationship?

Artificial Intelligence (AI) is a broader field of computer science that seeks to simulate human capabilities, such as reasoning, learning, and decision-making. Within this field, Machine Learning is an area that teaches systems to learn on their own from data.

In other words, all Machine Learning technology is part of the AI universe. It is this set of technologies that allows sanitation companies to automate leak detection, predict failures and make more effective decisions.

As Antonio Oliveira Jr., CTO and Co-Founder of Stattus4 explains:

“…What once seemed like science fiction is now transforming the way we live, work and care for the planet. AI has evolved from basic algorithms to systems capable of learning, predicting and even creating — and in doing so, it has opened up new possibilities for solving historic challenges for humanity.”

This strategic look reinforces how the combination of innovation and purpose can address historic challenges — and shows why technologies like Machine Learning are so crucial to the sector.

Audio and pressure as data source

At Stattus4, we apply Artificial Intelligence and Machine Learning to analyze data collected from the fields. They provide rich information about what is happening within the networks.

• A continuous low frequency sound?
• A repetitive peak during low consumption hours?
• An altered pressure during VMN (Minimum Nocturnal Flow)?
• A vibrational pattern that has appeared in previous leaks?

All of this becomes processable data — and, more importantly, a basis for decision-making. 

According to Antonio, Machine Learning in sanitation and Artificial Intelligence learning is continuous: the more data passes through the system, the more it learns, adjusts and anticipate the problem.

 The manager gains time, focus and agility

For those involved in day-to-day loss control, this means less unnecessary travel, more focus on critical areas, and responses based on field anomalies, not assumptions.

Imagine receiving prioritized alerts based on:

• Similarity to previous real leaks;
  
• Comparative analysis by region, soil type or time of occurrence;
  
• Crossing with maintenance history.

You no longer have to act in the dark. Data shows you where the risk lies — and what to do first.

 Talk to Stattus4

Want to understand how Machine Learning can apply to your network and its specific challenges?

Contact our team and see how to adapt our technologies to the reality of your dealership.