Demand Estimation In Water Distribution Systems: Solving Underdetermined Problems Using Genetic Algorithms

Abstract : Modeling of water distribution systems is fundamental for the design, analysis and operation of any water network. As with all hydraulic models, water demands are one of the most important input components in the model. However, estimation of the demand parameters is usually complicated due to the stochastic behavior of the water consumptions. Several methods have been proposed for estimating water demands. Most of them have been developed based on given frameworks where the number of unknown parameters is assumed to be equal or less than the number of measurements. The outcomes, therefore, rely on this assumption, which can lead to significant approximation errors in real water distribution systems. The approach proposed in this paper does not require the number of known inputs to be equal to the number of variables. In fact, nodes in the model could each have a different demand pattern. The genetic algorithm approach adopted here shows that the average results of multiple GA runs can estimate the demand patterns at each node. Moreover, the model can also be used to estimate the flow rates and nodal heads at non-measured locations of the water network, although the accuracy of the estimation depends on number, type and location of the measurements. Results are shown and discussed for a literature case study tested for a 24-hour time period.
Document type :
Journal articles
Liste complète des métadonnées

Cited literature [6 references]  Display  Hide  Download
Contributor : Import Ws Irstea <>
Submitted on : Thursday, June 29, 2017 - 11:33:37 AM
Last modification on : Wednesday, September 27, 2017 - 5:16:05 PM
Document(s) archivé(s) le : Thursday, January 18, 2018 - 2:50:54 AM


Publisher files allowed on an open archive




N. Do, A. Simpson, J. Deuerlein, O. Piller. Demand Estimation In Water Distribution Systems: Solving Underdetermined Problems Using Genetic Algorithms. Procedia Engineering, Elsevier, 2017, 186 (2017), pp.193-201. ⟨10.1016/j.proeng.2017.03.227⟩. ⟨hal-01550013⟩



Record views


Files downloads