Data-Driven Pressure Sensor Subset Selection for Long-Distance Water Transfer Pipelines: Q-DEIM Benchmarking with...

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 mH₂O) with only 15 sensors (75% reduction of the 60-sensor cleaned network); the stricter R2≥0.99 milestone requires K=26 for Q-DEIM and K=42 for the hybrid. PCA leverage, spatial and random sampling need 19, 32, and 43 sensors for R2≥0.95. At K=20, the hybrid concedes 0.003 mH₂O of RMSE for a 3.3× improvement in worst-case fill distance (5.86 km vs. 19.37 km). Regime coverage of the training library is the binding deployment constraint.