A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge

2026-05-11

AI Summary

This study addresses the vulnerability of aging urban drainage systems to combined sewer overflows (CSOs) during extreme rainfall events and the lack of real-time early-warning capabilities. To tackle this challenge, the authors propose a cloud-edge collaborative deep learning monitoring system that uniquely enables dual deployment of deep learning models on both cloud and edge devices. This architecture ensures continuous operation even during network outages and supports real-time visualization of manhole filling levels and overflow predictions through an interactive web dashboard. The system has been implemented in a publicly accessible online demonstration platform, significantly enhancing the real-time monitoring capacity and emergency response readiness of urban drainage infrastructure.

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).

Problem

• combined sewer overflow
• overflow monitoring
• extreme rainfall
• sewer system
• capacity exceedance

Innovation

• Deep Learning
• Edge Computing
• Cloud-Edge Integration
• Combined Sewer Overflow (CSO)
• Resilient Monitoring