Resilient Cloud-Edge Solution for Sewer Overflow Monitoring Using Deep Learning
Researchers have introduced a novel web-based demonstrator designed to monitor and forecast combined sewer overflows (CSO) in aging urban infrastructure. As extreme rainfall events increasingly stress historical city sewer systems, the risk of environmental contamination and public health hazards rises. This solution integrates Deep Learning forecasting methods to predict the filling dynamics of overflow basins, enabling timely preventive actions before capacity is exceeded. A key innovation of the system is its hybrid architecture, which operates across both cloud and edge computing settings. This design ensures the monitoring dashboard remains resilient and functional even during network outages, a critical feature for reliable infrastructure management. The project includes an interactive online demonstrator and a video showcase to illustrate its capabilities. By leveraging artificial intelligence for real-time analysis and robust data handling, this tool aims to mitigate the adverse impacts of CSOs. The research highlights the application of advanced AI techniques to solve pressing environmental and urban planning challenges, offering a scalable model for smart city infrastructure resilience.
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Resilient Cloud-Edge Solution for Sewer Overflow Monitoring Using Deep Learning
Researchers have introduced a novel web-based demonstrator designed to monitor and forecast combined sewer overflows (CSO) in aging urban infrastructure. As extreme rainfall events increasingly stress historical city sewer systems, the risk of environmental contamination and public health hazards rises. This solution integrates Deep Learning forecasting methods to predict the filling dynamics of overflow basins, enabling timely preventive actions before capacity is exceeded. A key innovation of the system is its hybrid architecture, which operates across both cloud and edge computing settings. This design ensures the monitoring dashboard remains resilient and functional even during network outages, a critical feature for reliable infrastructure management. The project includes an interactive online demonstrator and a video showcase to illustrate its capabilities. By leveraging artificial intelligence for real-time analysis and robust data handling, this tool aims to mitigate the adverse impacts of CSOs. The research highlights the application of advanced AI techniques to solve pressing environmental and urban planning challenges, offering a scalable model for smart city infrastructure resilience.
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