Discharges from combined sewer overflows (CSO) are unacceptable, particularly when they are not linked to wet weather. This paper presents an evaluation of an online artificial-intelligence-based analytics system to give early warning of such overflows due to system degradation. It integrates a cloud-based data-driven system using artificial neural networks and fuzzy logic with near real-time communications, taking advantage of the increasingly available real-time monitoring of water depths in CSO chambers. The data-driven system has been developed to be applicable to the vast majority of CSO and requiring a minimum period of data for training. Results are presented for a live assessment of 50 CSO assets over a six-month period, demonstrating continuous assessment of performance and reduction of CSO discharges. The system achieved a high true positive rate (86.7% on confirmed positives) and low false positive rate (3.4%). Such early warnings of CSO performance degradation are vital to proactively manage our aging water infrastructure and to achieve acceptable environmental, regulatory, and reputational performance. The system enables improved performance from legacy infrastructure without gross capital investment.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)