A cloud-based artificial intelligence (AI) system designed to detect blockages in sewers has shown an almost 90% accuracy rate in a recent trial. Early identification of sewer blockages is essential to reduce pollution incidents affecting our rivers.
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.
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 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.
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