A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge

TL;DR

This paper introduces a web-based system utilizing deep learning for sewer overflow forecasting, operable across cloud and edge environments, designed to improve resilience and enable timely interventions.

Contribution

It presents an integrated, resilient monitoring dashboard with deep learning forecasting for sewer overflows, adaptable to network disruptions.

Findings

Demonstrated a web-based monitoring system for sewer overflows.

Integrated deep learning forecasting in cloud and edge settings.

Provided an interactive dashboard for real-time overflow monitoring.

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 (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).