Case Study: Severn Trent Water achieves proactive sewer blockage detection with StormHarvester AI

Severn Trent Water avoids 11 pollution events with StormHarvester

Severn Trent Water, one of England and Wales' largest water and sewerage companies, sought to strengthen its ability to respond to sewer network alerts to prevent blockages before they occur. Their challenge was to move from a reactive to a proactive operational model to reduce pollution and flooding. To address this, they began a blockage detection trial with StormHarvester.

StormHarvester implemented its AI system, which ingested sewer level data and matched it with hyperlocal rainfall data to generate highly accurate, timely alerts. This allowed Severn Trent's operations team to prioritize and respond to potential issues proactively. The results were significant: during the five-month trial, 11 pollution events were avoided, 93 sites had remedial works completed, and 46 partial blockages were found and fixed, with zero events missed by the StormHarvester system.

Severn Trent Water