Estimating irregular water demands with physics-informed machine
  learning to inform leakage detection
Ivo Daniel Andrea Cominola
Leakages in drinking water distribution networks pose significant challenges
to water utilities, leading to infrastructure failure, operational disruptions,
environmental hazards, property damage, and economic losses. The timely
identification and accurate localisation of such leakages is paramount for
utilities to mitigate these unwanted effects. However, implementation of
algorithms for leakage detection is limited in practice by requirements of
either hydraulic models or large amounts of training data. Physics-informed
machine learning can utilise hydraulic information thereby circumventing both
limitations. In this work, we present a physics-informed machine learning
algorithm that analyses pressure data and therefrom estimates unknown irregular
water demands via a fully connected neural network, ultimately leveraging the
Bernoulli equation and effectively linearising the leakage detection problem.
Our algorithm is tested on data from the L-Town benchmark network, and results
indicate a good capability for estimating most irregular demands, with R2
larger than 0.8. Identification results for leakages under the presence of
irregular demands could be improved by a factor of 5.3 for abrupt leaks and a
factor of 3.0 for incipient leaks when compared the results disregarding
irregular demands.