An accurate hydraulic model of a water distribution network (WDN) is a critical prerequisite for
a multitude of operational, optimisation and planning tasks. The accuracy of a hydraulic model can
only be maintained through its periodic calibration and validation with acquired pressure and flow
data from a WDN. It is important that this process is robust and computationally efficient. This paper
describes the regularisation of an inverse problem to deal with data uncertainties and ill-posedness
of parameter estimation problems in WDNs. A novel data driven strategy is presented for tuning
the regularisation hyper-parameter for the inverse problem and also for validating the results on an
independent set of operational hydraulic data. A limited-memory quasi-Newton method (L-BFGS19 B) is implemented for solving the resulting regularised nonlinear inverse problem. Furthermore,
the implemented method utilises either the Darcy-Weisbach or Hazen-Williams head loss formulae,
and is investigated both with and without pipe grouping. An extensive experimental programme
was carried out to acquire unique hydraulic data from an operational WDN in order to investigate the
robustness of the proposed parameter estimation method. The hydraulic model of the operational WDN and the acquired hydraulic data are provided as supplementary data to enable the comparison
of hydraulic model calibration methods with operational data and encourage reproducible research