Optimal network calibration model based on genetic algorithms

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 demand adjustment factors in the context of an extended period analysis. 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 on an example water distribution system demonstrates the efficiency of the proposed approach.