Researchers Develop AI System to Monitor Sewer Overflows Using Cloud and Edge Computing
Researchers have created a web-based system that uses deep learning to forecast when combined sewer systems will overflow, addressing a growing problem in aging cities during extreme rainfall. The system operates across both cloud and edge computing infrastructure and includes a monitoring dashboard designed to function even during network outages. The technology could help cities prevent environmental damage and public health risks from sewage overflow events.
A team of researchers has developed an integrated monitoring solution for combined sewer overflow (CSO) events that leverages deep learning forecasting methods deployed across cloud and edge computing environments. The system addresses a critical infrastructure challenge facing many historical cities: aging combined sewer systems that are increasingly overwhelmed during extreme rainfall events, leading to environmental contamination and public health hazards. The solution features an interactive web-based dashboard that provides real-time monitoring and forecasting of overflow basin filling dynamics, with built-in resilience to network outages through its hybrid cloud-edge architecture. The work has been accepted for presentation at the 35th International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026) in the Demonstrations Track, and includes a video showcase demonstrating the system's functionality. By enabling timely prediction of capacity exceedance, the system aims to support preventive actions that could mitigate the impacts of combined sewer overflows.
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- arXiv cs.AICenter
A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge
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