Hydraulic network calibration using genetic optimization

The validity of a hydraulic network model depends not only on the accuracy of its physical and geometric data but also on the accuracy of certain parametric data such as pipe roughness coefficients and nodal demands. Difficulties associated with economical and reliable measurements for these parameters often dictate estimation of these parameters through model calibration. This paper describes an optimization approach to calibrate a network model for pipe roughness coefficients, and spatial as well as temporal demand adjustment factors. The proposed model obtains an optimal solution by minimizing a nonlinear objective function subject to a set of linear and nonlinear constraints using a powerful search technique based on a genetic algorithm. Application of the optimal calibration model to water distribution systems using synthetic calibration data demonstrates capabilities of the proposed algorithm to generate good solutions in an efficient and robust manner.