arXiv cs.AI· Zihang Ding, Amit Kumar, Imran Md. Azizul Islam, Mila Avellar Montezuma, Ruihang Zhang, Kun Zhang·· 3 小时前AI 评分34
数据驱动稀疏感知如何从稀疏测量重构城市排水管网水流：一项基于仿真的评估
Flow Reconstruction from Sparse Measurements in Urban Drainage Networks: A Simulation-Based Evaluation of Data-Driven Sparse Sensing
AI 导读
一项研究在 76 条管道的城市排水管网中评估了数据驱动稀疏感知（DSS），基于校准的 SWMM 模型仿真、SVD 与 pivoted QR 选择传感器布局并重构全网水流。
正文 · 原文
Abstract:Urbanization and increasingly frequent intense storms are placing stress on urban drainage networks. While dense monitoring is desirable, practical constraints in time, budget, and technology hinder its full implementation. How to monitor and predict flow conditions across the entire network under constrained resources is a major challenge. To address this, we evaluated data-driven sparse sensing (DSS), which selects sensor locations and reconstructs network-wide flow from the same reduced basis, in a 76-conduit urban drainage network. Simulations of a calibrated SWMM model were used to construct an event-weighted spatial basis using singular value decomposition (SVD), select rank-specific layouts using pivoted QR, and define the reconstruction decoder. Applied to 225 held-out simulations combining 25 plausible calibrated parameter sets with nine rainfall events, monitoring 5 of the 76 conduits (6.6%) achieved a median system-level Nash-Sutcliffe efficiency (NSE) of 0.992 (10th percentile 0.973) for observed storms, and a median NSE of 0.967 at the active unmonitored conduits. Pivoted QR performed comparably to genetic-algorithm layouts without requiring iterative layout searches, whereas largest-upstream-area placement failed with the DSS decoder and trailed QR even with a weighted regression decoder. The DSS flow reconstruction performance was insensitive to prescribed multiplicative Gaussian measurement noise, additive noise up to about 2 L/s and a 10 L/s detection floor, but showed location-dependent responses to loss of a monitored conduit, which a refitted regression decoder largely removed. This study supports the use of DSS, driven by physics-based simulations, for deciding how many flow sensors to install and where.
| Comments: | 28 pages, 8 figures. Partially presented at HydroML 2025 Symposium, Minnesota Water Resources Conference 2025, and AGU Fall Meeting 2025 |
| Subjects: | Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2511.04556 [cs.AI] |
| (or arXiv:2511.04556v4 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2511.04556 arXiv-issued DOI via DataCite |
Submission history
From: Kun Zhang [view email]
[v1]
Thu, 6 Nov 2025 17:08:19 UTC (3,554 KB)
[v2]
Fri, 22 May 2026 23:28:17 UTC (2,563 KB)
[v3]
Fri, 25 Sep 2026 13:49:30 UTC (2,252 KB)
[v4]
Tue, 10 Nov 2026 20:41:18 UTC (11,784 KB)
来源：arXiv cs.AI · arxiv.org