Multiobjective Sampling Design for Water Distribution Model Calibration

Abstract

Sampling design (SD) for water distribution systems (WDS) is undoubtedly an important issue, and has been addressed in the past by a number of scientists and practitioners. The aim of the SD methodology developed here is to find a set of optimal network locations at which to place measurement devices. Optimal locations are determined with the aim of collecting data that will be used later on in the calibration of the analyzed WDS hydraulic model. First, existing calibration and SD approaches in the case of WDS are reviewed. After that, SD is formulated as a two-objective optimization problem. The objectives are maximization of the calibrated model accuracy by minimization of the relevant uncertainties, and minimization of total SD costs. The optimal SD problem is then solved using a multiobjective genetic algorithm based on Pareto ranking, niching, and restricted mating. The methodology developed is applied and verified on a case study. At the end, a summary is made and relevant conclusions are drawn.