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Inflow and Infiltration (I&I) is one of the most persistent and complex challenges for wastewater network operators and is often the root cause of storm overflow spills. For Severn Trent Water, addressing I&I in the catchment was a potential solution to reduce the storage volume required to meet environmental targets and regulatory requirements. However, I&I is a moving problem where traditional methods are reliant on manual data analysis and manual inspections, which are time-consuming and costly.
InfoTiles, in collaboration with Arup, led a new approach to help Severn Trent Water address I&I in Gloucester, England, using AI and machine learning to enable data-driven decisions. Over a 16-week programme, InfoTiles focused on utilising existing data to uncover meaningful patterns and trends within numerous datasets, while Arup translated these insights into operational actions.
The partnership combined InfoTiles’ AI-driven analytics with Arup’s engineering insights and Severn Trent’s operational expertise, creating a synergy that unlocked the full potential of existing asset data.
I&I is a continuous battle, but by leveraging InfoTiles' advanced analytics capabilities, utilities can make smarter, faster, and more cost-effective decisions. Through this data-led approach, InfoTiles delivered clear, evidence-based insights to inform the next steps in tackling I&I in the catchment.
Our approach
The 16-week programme focused on utilising existing data, starting with cleaning the wastewater network GIS data before quantifying inflow and infiltration to combat I&I in Gloucester, England. By beginning with the available data, InfoTiles enabled a more intelligent and resilient wastewater network from day one.
Poor data quality in the underlying datasets can be a key difficulty in I&I management, often resulting in manual corrections to understand the existing network connectivity. InfoTiles directly addressed this challenge through the pioneering use of PipeFusion—a machine learning tool that identifies disconnected or mislabelled assets in wastewater networks. This demonstrated how ML can generate efficiencies and reduce the number of manual corrections required. The output from the PipeFusion analysis created a GIS network that could be fully leveraged for AI analytics.
SewerIntelligence, InfoTiles’ AI-powered analytics platform, was then applied to quantify and visualise inflow and infiltration. It proved that even with imperfect data, AI-driven insights can uncover tangible value. The quantification of I&I enabled Severn Trent to identify high-risk areas and prioritise future interventions where they would have the greatest impact, creating the maximum return on investment. The user interface instantly highlighted areas where interventions could offer higher or lower impact. The surplus treatment cost of I&I enabled cost-benefit analysis in the catchment, supporting targeted investment planning.
Scaling the AI approach across the Severn Trent Network
The success of the Gloucester initiative has demonstrated the potential of InfoTiles’ AI and ML-driven solutions for scaling across Severn Trent’s wider network. If integrated into real-time operations, this approach could enable Severn Trent to monitor the impact of interventions as they happen and analyse long-term catchment trends. This would not only strengthen future business cases but also demonstrate return on investment through the data itself. By digitalising I&I analysis with InfoTiles, Severn Trent is enabling proactive, data-driven decision-making and reinforcing their innovation principles of curiosity, courage, care, and pride.
By embracing digital transformation through the InfoTiles platform, Severn Trent is not only addressing immediate infrastructure challenges but also laying the foundation for a more resilient, sustainable, and intelligent wastewater network. As this AI-powered approach expands across the UK, it promises to revolutionise how utilities manage and mitigate the impact of inflow and infiltration—ensuring a more resilient future for water management.
Image Credit: Saffron Blaze