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This project demonstrates a practical application of Deep Learning for forecasting combined sewer overflow dynamics, integrating cloud and edge architectures to ensure system resilience against network outages. By combining predictive analytics with an interactive dashboard, the solution addresses critical environmental and public health risks in aging urban infrastructure. The work bridges machine learning capabilities with human-computer interaction to enable timely preventive actions in real-world scenarios.

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A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge

arXivJune 12, 20261 min read

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

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 monitoring, resilient to network outages. A video showcase is available online (this https URL).

3 pages, 6 figures, accepted at 35th International Joint Conference on Artificial Intelligence 2026 (IJCAI-ECAI 2026), Demonstrations Track. URL: this https URL

Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)

arXiv:2605.10592 [cs.AI]

(or arXiv:2605.10592v2 [cs.AI] for this version)

Full text under the publisher's open licence · 126 words

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