  This paper presents a novel algorithm for uncertainty quantification of water
distribution system measurement data including nodal demands/consumptions as
well as real pressure and flow measurements. This procedure, referred to as
Confidence Limit Analysis (CLA), is concerned with a deployment of a Least
Squares (LS) state estimator based on the loop corrective flows and the
variation of nodal demands as independent variables. The confidence limits
obtained for the nodal pressures and the inflows/outflows of a water network
are determined with the novel algorithm called Error Maximization (EM) method
and are evaluated with respect to two other more established CLA algorithms
based on an Experimental Sensitivity Matrix (ESM) and on the sensitivity matrix
method obtained with the LS nodal heads equations state estimator. The
estimated confidence limits obtained for two real water networks show that the
proposed EM algorithm is comparable to the other two CLA benchmark algorithms
but due to its computational efficiency it is more suitable for online decision
support applications in water distribution systems. Both ESM and EM methods
work for any operating point, whether arbitrarily or randomly chosen, for any
water network although EM method has the advantage of being computationally
superior and working with any sets of measurements.
