用数字孪生+稀疏传感优化城市排水系统测点，3个传感器就实现高精度流量重建。
Optimizing Sensor Placement for Flow Reconstruction in Urban Drainage Networks: A Digital Twin-Based Sparse Sensing Approach
- 基于数字孪生与奇异值分解，用QR分解筛选最优监测点。
- 仅3个传感器即达平均NSE 0.949，接近全局最优解。
- 对噪声和传感器失效有较好鲁棒性，适合资源受限的智慧水务应用。
由强降雨引发的城市内涝日益频繁。尽管高时空分辨率的洪水预测与监测是理想目标，但时间、预算与技术限制使其难以全面实施。如何在资源受限条件下有效监测城市排水网络并预测水流状态成为关键挑战。为此，本文提出一种数据驱动的稀疏传感（DSS）方法，以明尼苏达州杜卢斯市伍德兰流域为案例，构建数字孪生模型。通过将EPA-SWMM模型与奇异值分解及QR分解相结合，优化系统级流量重构的监测点布局。基于多场景SWMM模拟生成的水力数据，提取低维基底并识别信息量最大的传感器位置。跨事件验证显示，在77个候选节点中选取3个策略性传感器，即可在多个实测暴雨事件中达到平均0.949的系统级纳什-萨特克利夫效率（NSE）。QR选择的传感器集合与穷举搜索和蒙特卡洛随机配置的参考方案对比表明，其性能接近全局最优，显著优于随机布设。进一步评估了框架对乘性高斯噪声和单个传感器失效的鲁棒性：模型对噪声较不敏感，但传感器缺失的影响取决于数量与具体位置。
原文摘要 · Abstract (English)
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.
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