The goal of a water distribution system (WDS) is to supply the desired quantity of fresh water to consumers at the appropriate time. In order to properly operate a WDS, system operators need information about the system states, such as tank water level, nodal pressure, and water quality for the system wide locations. Most water utilities now have some level of SCADA (Supervisory Control and Data Acquisition) systems providing nearly real-time monitoring data. However, due to the prohibitive metering costs and lack of applications for the data, only portions of systems are monitored and the use of the SCADA data is limited. This dissertation takes a comprehensive view of real-time demand estimation in water distribution systems. The goal is to develop an optimal monitoring system plan that will collect appropriate field data to determine accurate, precise demand estimates and to understand their impact on model predictions. To achieve that goal, a methodology for real-time demand estimates and associated uncertainties using limited number of field measurements is developed. Further, system wide nodal pressure and chlorine concentration and their uncertainties are predicted using the estimated nodal demands. This dissertation is composed of three journal manuscripts that address these three key steps beginning with uncertainty evaluation, followed by demand estimation and finally optimal metering layout.;The uncertainties associated with the state estimates are quantified in terms of confidence limits. To compute the uncertainties in real-time alternative schemes that reduce computational efforts while providing good statistical approximations are evaluated and verified by Monte Carlo simulation (MCS). The first order second moment (FOSM) method provides accurate variance estimates for pressure; however, because of its linearity assumption it has limited predictive ability for chlorine under unsteady conditions. Latin Hypercube sampling (LHS) provides good estimates of prediction uncertainty for chlorine and pressure in steady and unsteady conditions with significantly less effort.;For real-time demand estimation, two recursive state estimators; tracking state estimator (TSE) based on weighted least squares (WLS) scheme and Kalman filter (KF), are applied. In addition, in order to find available field data types for demand estimation, comparative studies are performed using pipe flow rate and nodal pressure head as measurements. To reduce the number of unknowns and make the system solvable, nodes with similar user characteristics are grouped and assumed to have same demand pattern. The uncertainties in state variables are quantified in terms of confidence limits using the approximate methods (i.e., FOSM and LHS). Results show that TSE with pipe flow rates as measurements provide reliable demand estimations. Also, the model predictions computed using the estimated demands match well with the synthetically generated true values.;Field measurements are critical elements to obtaining quality real-time state estimates. However, the limited number of metering locations has been a significant obstacle for the real-time studies and identifying locations to best gain information is critical. Here, an optimal meter placement (OMP) is formulated as a multi-objective optimization problem and solved using a multi-objective genetic algorithm (MOGA) based on Pareto-optimal solutions. Results show that model accuracy and precision should be pursued at the same time as objectives since both measures have trade-off relationship. GA solutions were improvements over the less robust methods or designers' experienced judgment.
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