Calibration of Water Demand Multipliers in Water Distribution Systems Using Genetic Algorithms
Publication: Journal of Water Resources Planning and Management
Volume 142, Issue 11
Abstract
Hydraulic models have been widely used for design, analysis, and operation of water distribution systems. As with all hydraulic models, water demands are one of the main parameters that cause the most uncertainty to the model outputs. However, the calibration of the water demands is usually not feasible attributable to the limited quantity of available measurements in most real water networks. This paper presents an approach to calibration of the demand multiplier factors under an ill-posed condition where the number of measurements is less than the number of parameter variables. The problem is solved using a genetic algorithm (GA). The results show that not only is the GA able to match the calibrated values at measured locations, but by using multiple runs of the GA model, the flow rates and nodal heads at nonmeasured locations can be estimated. Three case studies are presented as an illustration of the problem. The first case study is a small network that demonstrates the calibration model. The second case study shows a comparison between the genetic algorithm model and a singular value decomposition model. The last case study is a large network that allows for practical considerations in applying the proposed methodology to a realistic context.
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Published In
Journal of Water Resources Planning and Management
Volume 142 • Issue 11 • November 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Nov 3, 2015
Accepted: Apr 11, 2016
Published online: Jun 22, 2016
Published in print: Nov 1, 2016
Discussion open until: Nov 22, 2016
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