# A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge

arXiv: https://arxiv.org/abs/2605.10592 · HTML: https://mlsanity.com/paper/2605.10592/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 3 of 3)

- [Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation](https://mlsanity.com/paper/2408.11619/) (2024)
- [Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management](https://mlsanity.com/paper/2508.13905/) (2025)
- [Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience](https://mlsanity.com/paper/2504.17461/) (2025)

## Abstract (arXiv)

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 monitoring, resilient to network outages. A video showcase is available online (https://cloud.bht-berlin.de/index.php/s/b9xt4T3SdiLBiFZ).