[Blog] Rising to the Pollution Challenge with Machine Learning

Better analysis of existing data on rising mains can mitigate risk of pollution incidents, says George Heywood, analytics innovation lead. 

The Government has left the water companies and Ofwat in no doubt that they need to do more to protect the environment from pollution and allay the concerns of stakeholders. 

In a statement published on 2 February 2022, environment minister Rebecca Pow said water quality was an absolute priority and also called for measures to “reduce harm from storm overflows, improve monitoring and reporting of pollution incidents making this more transparent, to tackle run-off from agriculture, and protect the health of our rivers and seas.” 

Rising mains 

Rising main sewers are one asset set that requires focus. These are high risk, critical assets, but with many in the UK ageing and becoming more vulnerable to bursts, historic programmes of proactive maintenance and investment may no longer be enough to keep up with the rate of deterioration. 

Often situated in hard-to-reach, remote locations, a burst rising main can have catastrophic ecological impact. Technical and logistical limitations in monitoring can mean utilities are alerted – often by a member of the public - hours or even days into the event. This is too late to take action that would prevent a pollution event. 

Given the mounting stakeholder pressure and water companies’ own commitments to cut pollutions, it is unsurprising they are working closely with the supply chain to develop innovative solutions. One example from Ovarro is BurstDetect, a cloud-based early warning system that was developed in collaboration with UK utilities. 

The tool detects rising main bursts with potential to cause pollution incidents. Through a dashboard, it provides an overview of pumping station status and both ongoing and historical events. If data suggests a potential burst, an alert is sent to control rooms often within an hour of occurrence. 

This ensures users are able to make swift, informed decisions and quickly allocate resources to reduce environmental impact. Such early action can prevent the escape of sewage and resulting environmental damage, ensuring companies fulfil their environmental obligations and avoid fines, regulatory penalties and prosecutions. 

With so much available water and wastewater network data, it is just not possible for humans to process and analyse the information themselves. By having the correct technology and processes in place, the stage will be set for utilities to rapidly increase their real-time and predictive capabilities and deliver on their major environmental commitments.

To find out more about BurstDetect, check out our recent webinar "Preventing Pollution from Rising Mains through Machine Learning".