Uniting for a cleaner future: UK leads the way with cloud-based AI to reduce river pollution

A recent trial of a cloud-based AI system designed to detect blockages in sewers achieved an accuracy rate of almost 90 percent. Early identification of sewer blockages is crucial for reducing pollution incidents that harm our waterways.

The project is a collaboration between the University of Sheffield, Yorkshire Water, and tech company Siemens, and is a part of the ‘Pollution Incident Reduction Plan’ which focuses on early intervention to reduce pollution incidents by 50 percent by 2025.

Sewers have ‘combined sewer overflows’ (CSOs) which let excess water spill out into a nearby water body when the pipes are full due to heavy rainfall, preventing downstream flooding. These spillages can also be caused by unexpected restrictions in the pipe, such as blockages, resulting in unnecessary pollution of our rivers and watercourses.

Sensors monitor water depth in the CSOs, and other parts of the sewer network, allowing real-time understanding of performance. The quantity of sensors makes manual analysis infeasible, hence an automated system is needed. 

The technique was originally developed by the University of Sheffield and Yorkshire Water to improve on their previous analytics technique. This project with Siemens has further developed the tool into a commercial, cloud-based solution - the Siemens Water (SIWA) Blockage Predictor.

The AI-based solution predicts water depths using rainfall data and compares these to the measured depth using a Fuzzy Logic (FL) algorithm. The FL alerts the water utility of any unexpectedly high depths that could lead to a pollution incident. The aim is to identify developing blockages so that they can be removed before pollution occurs.

A new peer-reviewed journal article presents an assessment of the SIWA Blockage Predictor for 50 CSOs over a two-year ‘historic’ period and a six-month ‘live’ period. The article also compares performance to the previous analytics solution. 

Across the full dataset, 88.4 percent of confirmed issues were correctly identified, compared to 26.6 percent for the previous solution. The full article, entitled Cloud-Based Artificial Intelligence Analytics to Assess Combined Sewer Overflow Performance, published in the Journal of Water Resources Planning and Management can be read by visiting: https://dx.doi.org/10.1061/JWRMD5.WRENG-5859 

Dr. Will Shepherd, Principal Investigator from the University of Sheffield’s Department of Civil and Structural Engineering, said: “Our sewer networks were not designed to convey heavy rainfall to treatment, CSOs provide an essential relief valve when the rain would otherwise cause flooding further down the network.  Our focus here is on making them as environmentally friendly as possible by identifying blockages which would cause premature spills and hence pollution of rivers and watercourses.”

Professor Joby Boxall, Professor of Water Infrastructure Engineering in the University of Sheffield’s Department of Civil and Structural Engineering, said: "The synergies of the collaborative partnership approach to this research was vital to success. It was important that the different needs and ambitions of each partner were mutually recognized and respected from the outset and that we built and maintained a high level of trust."

Dr Stephen Mounce, Director of Mounce HydroSmart said: “This project has demonstrated how the application of AI and data analytics can progress from research prototypes in early-stage projects to a mature, generic solution deployed on a cloud-based platform. It has been exciting to see the real-world deployment of the system to over 2,000 assets at Yorkshire Water.”

Dr John Gaffney, Product Owner of SIWA Blockage Predictor, said: “This collaboration has been a fantastic example of how a technology company can take high Technology Readiness Level (TRL) research from a University, productize it, and prove value via peer-reviewed science to an end user. The fact the product is serving such an important purpose in protecting watercourses is particularly rewarding.”

The UK's development of cloud-based AI to help reduce river pollution is a major step forward in the fight against environmental degradation. By using this technology to monitor and reduce pollution levels, the UK is leading the way in the global effort to protect our rivers and other waterways. This is a great example of how technology can be used to help protect our planet, and it is a reminder that we all have a role to play in preserving our environment. With this new technology, we can all do our part to ensure that our rivers remain healthy and clean for generations to come.