Cloud-Based Artificial Intelligence Analytics to Assess Combined Sewer Overflow Performance

Abstract

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. Combined sewerage networks convey wastewater from residential and commercial properties as well as rainfall runoff from urban catchments. The CSO provides a relief valve when runoff from rainfall would overwhelm the downstream network and treatment works. Excess water is spilled into a nearby watercourse, ideally when the watercourse flow has increased to provide additional dilution and thus minimize impacts. If a blockage or other defect downstream of a CSO results in a decrease in discharge capacity, the CSO can spill earlier than it is designed to or even in dry weather. Prior to the deployment of level sensors, such premature spills could only be identified through a visible spill or water quality impact. Sensors allow water utilities to monitor depths in CSO chambers; however, each utility will have a large number of CSO, which means that an automated system is needed to identify premature spills. This paper discusses the development and validation results obtained from a pilot deployment of a data analytics solution to identify abnormal water depths in a CSO.