Flow Reconstruction from Sparse Measurements in Urban Drainage Networks: A Simulation-Based Evaluation of Data-Driven Sparse Sensing
Zihang Ding, Amit Kumar, Imran Md. Azizul Islam, Mila Avellar Montezuma, Ruihang Zhang, Kun Zhang
原文摘要
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.
cs.AI · cs.CE