Its main characteristics
are as follows:

The sampling design
problem is formulated as a multi-objective optimisation problem. The two
main objectives are:

(a) maximise calibration accuracy by minimising calibrated model
uncertainty and (b) minimise total sampling design costs.

Three calibration accuracy
objectives were analysed:

(1) D-optimality,

(2) A-optimality and

(3) V-optimality. Therefore, both model parameter and prediction
uncertainties were analysed.

A new single-objective GA
(SOGA) optimal sampling design model was developed. The sampling design
problem was formulated as a single-objective problem and solved using a
standard GA optimisation method. Two objectives are recombined into a
weighted single one after normalisation.

A new multi-objective GA (MOGA)
optimal sampling design model was developed based on Pareto domination,.
The aim was to treat and solve the sampling design problem as a true
multi-objective optimisation problem. The MOGA methodology is based on
Pareto domination rules, restricted mating and niching. The new MOGA
approach was compared to several well-known SD methods from the
literature.

All developed calibration
and sampling design approaches were tested and verified on multiple case
studies involving both relatively simple, small artificial WDS networks
and relatively large, complex real-life WDS networks.