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.
Practical Applications
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.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data). Data supplied by the WSP are subject to a nondisclosure agreement; the code is commercially confidential.
Acknowledgments
The authors are grateful for the support, access to data, and funding provided by Siemens PLC and Yorkshire Water Services Ltd. and their permission to publish the details included herein.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 149 • Issue 10 • October 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 25, 2022
Accepted: May 11, 2023
Published online: Jul 26, 2023
Published in print: Oct 1, 2023
Discussion open until: Dec 26, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Asset management
- Business management
- Chemical degradation
- Chemical processes
- Chemistry
- Combined sewers
- Computer networks
- Computer programming
- Computing in civil engineering
- Environmental engineering
- Financial management
- Flow (fluid dynamics)
- Fluid dynamics
- Fluid mechanics
- Fuzzy logic
- Hydrologic engineering
- Infrastructure
- Internet
- Lifeline systems
- Neural networks
- Overflow
- Practice and Profession
- Sewers
- Water and water resources
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