How small a water-main leak can pressure sensors find, and does a changepoint detector beat a threshold or CUSUM?

research/infra-waterloss

Water-leak detection from pressure residuals on EPANET's Net3 (WNTR): at a 5% weekly false-alarm budget, 5 sensors and 10% demand error, CUSUM finds all leaks the sensors can see at 4% of demand but 8% at 1% (3% of all leaks); demand error, not sensor noise or detector choice, sets the limit (simulated leaks; BattLeDIM L-Town was unreachable).

6 tests

What it found

WNTR simulates one week of hourly pressures on EPANET’s Net3 (92 junctions) with leaks of 0.25–4 % of mean demand (1.7–28 L/s) opened at hour 96 at 60 random junctions, against a nominal model whose demands are wrong (node error sd 5–20 %, system-wide AR(1) error sd 1–5 %) and sensors with noise 0.05–1 m. A moving-average threshold, CUSUM and PELT (changepoint) each get a threshold calibrated on 120 leak-free weeks to at most 5 % of weeks with any alarm, with and without a regression on a noisy inflow meter. With 5 greedily placed sensors and base demand error, the best detector (flow-adjusted CUSUM) finds 100 % of the leaks the sensors can see (28 % of locations) at 4 % of demand but only 8 % at 1 % and 85 % at 2 %, and across all leak locations 3 % at 1 % and 38 % at 4 %. Demand error moves the result far more than sensor noise (covered 2 % leaks: 92 % at low demand error, 85 % at base, 8 % at high), and averaging 15 sensors instead of 5 hurts. PELT did not beat CUSUM on 2 % leaks in any condition at the same false-alarm budget. Limits: the BattLeDIM L-Town network and its leak schedule could not be downloaded here, so there is no comparison with the competition’s methods; Net3 is a textbook network; leaks are simulated, demand error and inflow-meter noise (0.5 %) are assumed and swept, not measured; 60 locations per size, so rates carry binomial error of up to ±0.12.

A figure from its own output

Figure produced by research/infra-waterloss: detection — infra-waterloss/experiments/detection.svg, written by the project's own experiment code.

Paper, code and results

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.