A cloud-based artificial intelligence system designed to detect blockages in sewers has shown an almost 90% accuracy in a recent trial, according to Sheffield University in England.

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% by 2025.

Sewers have combined sewer overflows 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.

“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," says Will Shepherd, principal investigator from the University of Sheffield’s Department of Civil and Structural Engineering. "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 AI based solution predicts water depths using rainfall data and compares these to the measured depth using a Fuzzy Logic 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.

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% of confirmed issues were correctly identified, compared to 26.6% 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 is available here.