A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge
For municipalities managing aging sewer systems, this provides a practical tool for anticipating overflow events and enabling timely preventive actions.
The paper presents a web-based demonstrator integrating deep learning forecasting methods for combined sewer overflow monitoring across cloud and edge settings, resilient to network outages.
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 (https://riwwer.demo.calgo-lab.de) 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).