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 (this https URL) 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).
| Comments: | 3 pages, 6 figures, accepted at 35th International Joint Conference on Artificial Intelligence 2026 (IJCAI-ECAI 2026), Demonstrations Track. URL: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.10592 [cs.AI] |
| (or arXiv:2605.10592v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10592 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vipin Singh [view email]
[v1]
Mon, 11 May 2026 14:00:25 UTC (1,393 KB)