ArticleSensors (Basel, Switzerland)2026
Data-Driven Pressure Sensor Subset Selection for Long-Distance Water Transfer Pipelines: Q-DEIM Benchmarking with Spatial-Diversity Refinement.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Long-distance water pipelines are typically instrumented by engineering convention, producing dense and partially redundant networks. Using 7239 hourly snapshots from a 122 km trunk pipeline in northeast China (72 deployed pressure sensors; 60 retained after a >30% missing-rate filter), we ask how few sensors are needed to reconstruct the full pressure field. The field has effective rank 15 at the 99% cumulative-variance level, so the cleaned network is over-sampled by roughly 4×. We benchmark four selection strategies against a random baseline-spatial farthest-point, PCA leverage, Q-DEIM, and a proposed hybrid that adds a soft spatial-diversity penalty to the Q-DEIM residual score-under a uniform Tikhonov-regularised gappy POD reconstruction. Q-DEIM and the hybrid both reach R2=0.982 (RMSE 0.96 mH
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