An artificial intelligence (AI) system has been put through it paces to see how it performs at detecting blockages in sewers – and apparently aced the task with an almost 90% accuracy.
Early identification of sewer blockages is essential to reduce pollution incidents affecting our rivers, so systems capable of doing exactly that are critical.
The project to design the cloud-based AI able to detect blockages is a collaboration between experts at Sheffield University, Yorkshire Water, and tech firm Siemens. It forms part of the ‘Pollution Incident Reduction Plan’, which focuses on early intervention to reduce pollution incidents by 50% 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.
Dr Will Shepherd, principal investigator from the university’s Department of Civil & Structural Engineering, said: “Our sewer networks were not designed to convey heavy rainfall to treatment, CSOs provide an essential relief valve when 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.”
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 which could lead to a pollution incident. The aim is to identify developing blockages so that they can be removed before pollution occurs.
Professor Joby Boxall, professor of water infrastructure engineering at Sheffield’s Department of Civil & 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 was mutually recognised and respected from the outset and that we built and maintained a high level of trust.”
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.
It was found that 88.4% of confirmed issues were correctly identified, compared to 26.6% for the previous solution.
The article, Cloud-Based Artificial Intelligence Analytics to Assess Combined Sewer Overflow Performance, was published in the Journal of Water Resources Planning and Management.
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 the SIWA Blockage Predictor, added: “This collaboration has been a fantastic example of how a technology company can take high Technology Readiness Level (TRL) research from a university, productise 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.”
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