Getting the most out of data
The overall aim of the project was to understand where I&I is present to prioritise where to invest and where not to. While technologies such as machine learning or AI could help identify patterns, we first needed to ensure we had good-quality data. We partnered with InfoTiles, using PipeFusion, a machine learning algorithm, to review and update Severn Trent Water’s Geographic Information System data. We also used Sewer Intelligence to quantify I&I volumes, and the associated economic and carbon costs. Existing records were combined with additional datasets, including telemetry, rainfall, river gauge data and pump activations. This allowed us to estimate I&I by zone and produce data-enabled insights.