Urban flooding triggered by intense rainfall is becoming increasingly frequent and widespread. While flood prediction and monitoring in high spatio-temporal resolution are desired, practical constraints in time, budget, and technology hinder its full implementation. How to monitor urban drainage networks and predict flow conditions under constrained resources is a major challenge. To address this, we introduced a data-driven sparse sensing (DSS) approach, demonstrated via a digital-twin of the Woodland catchment in Duluth, Minnesota. Specifically, we coupled EPA-SWMM with singular value decomposition and QR factorization-based sensor selection to optimize monitoring locations for system-level flow reconstruction. An ensemble of SWMM simulations, driven by diverse scenarios, provided the necessary hydraulic data to extract the reduced basis and identify informative sensor locations. Cross-event validation showed that three strategically placed sensors among 77 candidate nodes achieved a mean system-level Nash-Sutcliffe efficiency (NSE) of 0.949 across observed storm events. The QR-selected sensor sets were benchmarked against reference sensor configurations obtained from exhaustive searches and Monte Carlo random-placements. This comparison further showed that flow reconstruction based on QR-selected sensors closely tracked the exhaustive optimum while substantially outperforming random placements. We further evaluated the framework's robustness by introducing multiplicative Gaussian noise and simulating individual sensor failures. While the model is relatively resilient to noise, the impact of sensor dropouts depends heavily on the number of sensors allocated and their specific locations.

翻译：强降雨引发的城市洪水日益频繁且影响范围不断扩大。尽管高时空分辨率的洪水预测与监测需求迫切，但时间、预算和技术方面的现实约束阻碍了其全面实施。如何在资源受限条件下监测城市排水管网并预测水流状态是一项重大挑战。为此，我们提出了一种数据驱动的稀疏传感（DSS）方法，并通过明尼苏达州德卢斯市伍德兰汇水区的数字孪生进行了验证。具体而言，我们将EPA-SWMM与基于奇异值分解及QR分解的传感器选择相结合，以优化监测点位，实现系统级的水流重建。由多样场景驱动的SWMM集成模拟提供了必要的水力数据，用于提取降阶基并识别信息量丰富的传感器位置。跨事件验证表明，在77个候选节点中策略性地部署3个传感器，即可在观测到的风暴事件中实现平均系统级纳什-苏特克利夫效率（NSE）0.949。我们将QR选取的传感器组与通过穷举搜索和蒙特卡洛随机放置获得的参考传感器配置进行了基准对比。该比较进一步表明，基于QR选取传感器的水流重建结果与穷举最优方案高度吻合，同时显著优于随机放置方案。我们通过引入乘性高斯噪声并模拟单个传感器故障，进一步评估了该框架的鲁棒性。虽然模型对噪声具有相对韧性，但传感器失效的影响在很大程度上取决于分配传感器的数量及其具体位置。