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).

翻译：许多历史悠久的城市中老化的合流制排水系统因极端降雨事件而承受日益加剧的压力，这可能导致合流制溢流（CSO），进而对环境和公共健康产生显著影响。预测溢流池的充填动态对于预估容量超限并及时采取预防性CSO应对措施至关重要。我们提出一个基于网页的演示系统（https://riwwer.demo.calgo-lab.de），该平台将云端与边缘侧的深度学习预测方法集成至交互式监测仪表板中，用于溢流监测，并具备网络中断下的韧性。线上视频展示见：https://cloud.bht-berlin.de/index.php/s/b9xt4T3SdiLBiFZ。