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).

翻译：许多历史城市的合流制下水道系统正日益受到极端降雨事件的压力，这些事件可能引发合流制下水道溢流（CSO），对环境和公共健康造成重大影响。预测溢流池的充填动态对于预估容量超限以及及时采取预防性CSO行动至关重要。我们展示了一个基于网络的演示器，它将云和边缘环境中的深度学习预测方法集成到一个交互式监测仪表板中，用于溢流监测，并对网络中断具有韧性。相关视频演示可在线获取（https://cloud.bht-berlin.de/index.php/s/b9xt4T3SdiLBiFZ）。