Tracking and measuring the invisible
Our sewerage systems are hidden almost entirely from the outside world. But that doesn’t mean we can’t measure and monitor them. Today, a huge number of measuring points and sensors in the system help us understand and maintain unbroken visibility of what’s really going on underground.
They supply daily flows of data relating to everything from water levels and pump frequencies, to system energy consumption. But despite this modern approach to monitoring, most of this data still has to be interpreted manually by a trained professional – making it both a time-consuming and error-prone process.
That’s where data science can help. A machine learning model can process and interpret the raw data gathered from sensors, providing sewerage managers with immediate visibility of patterns and correlations. These models can then self-learn over time, using algorithms to understand past events and forecast more accurately for the future. Plus, they make it simple to establish relationships between complex variables and influencing characteristics such as the weather and pumping capacity.