arXiv:2605.10592v2 Announce Type: replace-cross Abstract: Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows (CSO) with significant environmental and public health impacts. Forecasting the filling dynamics of overflow basins is critical for anticipating capacity exceedance and enabling timely preventive actions for CSO. We present a web-based demonstrator that integrates Deep Learning forecasting methods in both cloud and edge settings into an interactive monitoring dashboard for overflow monit
Climate change is increasing extreme rainfall events, stressing aging infrastructure, and driving the immediate need for advanced monitoring solutions.
This development allows for proactive management of critical urban infrastructure, mitigating environmental and public health risks associated with sewer overflows.
The integration of Deep Learning in both cloud and edge settings for real-time sewer overflow monitoring provides a more resilient and predictive infrastructure management capability.
- · Smart city solution providers
- · Urban infrastructure authorities
- · Environmental engineering firms
- · AI/ML providers
- · Cities with outdated infrastructure
- · Companies relying on reactive maintenance
- · Public health organizations impacted by overflows
Improved prediction and preventative action against combined sewer overflows (CSOs) will reduce environmental pollution and public health hazards.
The successful deployment of such resilient cloud-edge AI solutions will accelerate their adoption across other critical municipal infrastructure management, like power grids or traffic systems.
The demonstrated value of AI-driven infrastructure resilience could influence increased public and private investment into smart city technologies and climate adaptation strategies.
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Read at arXiv cs.LG