Sequential Estimation of Demand and Roughness in Water Distribution System

Abstract

Pipe roughness and water consumption at a demand node are the most uncertain input variables in a simulation model because they are not typically directly measurable. Instead, information provided from field measurements are used to estimate them indirect way. Parameter estimation is the process of adjusting model parameters so that the simulation model represents the real system adequately by fitting the model output to the field data. To provide more accurate estimates and account for all associated uncertainties, the two variables, i.e., demand and roughness, must be estimated simultaneously. This study proposes a two-step sequential method for dual estimation of demand and roughness coefficient based on a weighted least squares (WLS) scheme using field measurements of pipe flow rates and nodal pressure heads under multiple demand loading conditions. The algorithm is applied to a simple hypothetical system using synthetically generated field data. The proposed two-step sequential model provides accurate estimates with little effort in terms of simulation time.