A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge

Researchers have developed a web-based monitoring system that combines deep learning forecasting with cloud and edge computing to predict combined sewer overflow (CSO) events in aging urban infrastructure. The system operates as a resilient dashboard capable of functioning during network outages, addressing a critical infrastructure challenge exacerbated by extreme weather events in historical cities.

Combined sewer overflow events represent a significant environmental and public health challenge in densely populated urban areas with aging infrastructure. When extreme rainfall exceeds system capacity, untreated sewage flows directly into waterways, contaminating water supplies and harming ecosystems. This research addresses the forecasting gap by deploying deep learning models across distributed computing architectures—both centralized cloud resources and localized edge devices—enabling real-time prediction of basin filling dynamics without complete dependence on continuous connectivity.

The hybrid cloud-edge approach reflects broader trends in critical infrastructure modernization, where redundancy and resilience become paramount as climate-driven extreme weather intensifies. Cities worldwide face similar CSO challenges, with aging sewer systems designed for historical precipitation patterns now overwhelmed by contemporary rainfall intensity. The demonstrator platform represents practical infrastructure-as-a-service innovation, moving beyond theoretical models to operational solutions.

For municipalities and water management agencies, this system enables proactive intervention before overflow occurs, reducing environmental contamination and supporting compliance with increasingly stringent water quality regulations. The edge-computing capability is particularly valuable in regions with unreliable network infrastructure, ensuring operational continuity during outages. Development teams and infrastructure operators can leverage these deep learning techniques to improve forecasting accuracy across other environmental monitoring domains.

Future deployment at scale will depend on integration costs, model accuracy validation across diverse sewer system designs, and adoption by municipal authorities. The research demonstrates how AI-driven analytics can address traditional infrastructure challenges, creating value through prevention rather than reactive remediation.

• →Deep learning forecasting integrated with cloud and edge computing enables real-time combined sewer overflow prediction with network outage resilience.
• →Hybrid computing architecture addresses the critical infrastructure gap where aging sewer systems face unprecedented stress from climate-driven extreme weather events.
• →The demonstrator platform enables proactive intervention before overflow occurs, reducing environmental contamination and supporting regulatory compliance.
• →Edge computing deployment ensures operational continuity in regions with unreliable network infrastructure, expanding applicability to diverse urban environments.
• →This infrastructure monitoring approach represents a scalable template for applying AI analytics to other environmental and utility management challenges.