Southern Water is the private utility company responsible for the public wastewater collection and treatment in Hampshire, the Isle of Wight and West Sussex, East Sussex and Kent, covering a total population of over 4.7 million people.
Wastewater utilities are continuously exploring ways of enhancing protection to the environment by reducing their number of sewer overflows into watercourses. For Southern Water, this involved an analysis of what was causing spills by looking at Event Duration Monitor (EDM) spill data.
THE CHALLENGE
Inflow and infiltration (I&I) can cause significant problems for wastewater assets and their operators. When sea water, river water, and the infiltration of groundwater enters the wastewater network, it can result in:
- Increased overflows.
- Damage to sewer network infrastructure.
- Reduced network capacity for incoming sewage.
- Imbalance of microbiome in the treatment works.
- The need to treat larger volumes of wastewater than necessary leading to increased costs and carbon footprint.
THE SOLUTION
Working with Southern Water and Stantec, StormHarvester used machine learning and hyperlocal rainfall to characterise overflows and their determining factors.
Each site was considered separately to allow for models to learn and predict site-specific patterns and behaviours. This approach gives more accurate predictions of how sites will perform leading to a higher confidence in the comparison of predicted vs actual behaviour after improvements have been implemented.
RESULTS
StormHavester discovered that a large number of overflows came from a small number of sites, and 25% of overflows occurred at sites with I&I present.
The correlation to rainfall was not directly apparent for I&I and further analysis found a correlation existed with levels and rates of change of groundwater sewer levels. If a site is reactive to rainfall, you would expect the site to not overflow with lower quantities of rainfall and begin to spill with larger quantities.
At sites with I&I, the site doesn’t overflow during the summer months with larger rainfall events but overflows continuously from January to April with considerably smaller rainfall events.
– Dr. Nick Mills, Head of Storm Overflow Task Force, Southern WaterThe I&I tool has not only provided us with another tool to help reduce spills and pollution, but a data driven tool to help scope, size and prioritise the right solutions and investment. Another win for machine learning.
