A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge
cs.AI, cs.HC, cs.LG
Submitted: 2026-05-11
Updated: 2026-06-10
Comments: 3 pages, 6 figures, accepted at 35th International Joint Conference on Artificial Intelligence 2026 (IJCAI-ECAI 2026), Demonstrations Track. URL: https://riwwer.demo.calgo-lab.de
Code: https://github.com/calgo-lab/resilient-timeseries-evaluation
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: 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
Terminology
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).
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