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arXiv AI By Zihang Ding, Amit Kumar, Imran Md. Azizul Islam, Mila Avellar Montezuma, Ruihang Zhang, Kun Zhang

Flow Reconstruction from Sparse Measurements in Urban Drainage Networks: An Application and Evaluation of Data-Driven Sparse Sensing

Read the original on arXiv AI →

The study evaluates a data‑driven sparse sensing (DSS) workflow for monitoring urban drainage networks, using a 77‑node model to optimize sensor placement and reconstruct flow conditions. By applying singular value decomposition, pivoted QR selection, and a reconstruction decoder to 225 simulated scenarios, a 3‑node layout (4 % of the network) achieved median Nash‑Sutcliffe efficiency of 0.791, with all cases exceeding 0.700. The method matched performance of Greedy D‑optimal and genetic algorithm approaches, proved robust to Gaussian noise, and identified that sensor loss sensitivity correlates with upstream drainage area and conduit characteristics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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