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Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks

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Abstract

The success of hydraulic simulation models of water distribution networks is associated with the ability of these models to represent real systems accurately. To achieve this, the calibration phase is essential. Current calibration methods are based on minimizing the error between measured and simulated values of pressure and flow. This minimization is based on a search of parameter values to be calibrated, including pipe roughness, nodal demand, and leakage flow. The resulting hydraulic problem contains several variables. In addition, a limited set of known monitored pressure and flow values creates an indeterminate problem with more variables than equations. Seeking to address the lack of monitored data for the calibration of Water Distribution Networks (WDNs), this paper uses a meta-model based on an Artificial Neural Network (ANN) to estimate pressure on all nodes of a network. The calibration of pipe roughness applies a metaheuristic search method called Particle Swarm Optimization (PSO) to minimize the objective function represented by the difference between simulated and forecasted pressure values. The proposed method is evaluated at steady state and over an extended period for a real District Metering Area (DMA), named Campos do Conde II, and the hypothetical network named C-town, which is used as a benchmark for calibration studies.

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References

  • Alvisi S, Franchini M (2010) Pipe roughness calibration in water distribution systems using grey numbers. J Hydroinf 12(4):424–445. doi:10.2166/hydro.2010.089

    Article  Google Scholar 

  • Brentan BM, Luvizotto Júnior E (2013) PSO applied to reduce the cost of energy in water supply networks. Appl Mech Mater 409–410:703–706. doi:10.4028/www.scientific.net/AMM.409-410.703

    Article  Google Scholar 

  • Brentan B, Campbell E, Izquierdo I, Pérez-García R, (2015). Calibración conjunta de presiones y fugas en redes de abastecimiento de agua potable. Conference: XI Seminario Euro Latino Americano de Sistemas de Ingeniería – XI SELASI, At Huaraz, Peru

  • Cheng W, He Z (2011) Calibration of nodal demand in water distribution systems. J Water Resour Plan Manag 137(1):31–40. doi:10.1061/(ASCE)WR.1943-5452.0000093

    Article  Google Scholar 

  • Christodoulou S, Ellinas G (2011) Ant Colony optimization for level of service improvements in piping networks. European Water 33:3–10

    Google Scholar 

  • Cordoba GAC, Tuhovcáka L, Tausa M (2014) Using artificial neural network models to assess water quality in water distribution networks. In: 12th international Conference on computing and control for the water Industry, Perugia, Italia, December 70: 399–408

  • Dini M, Tabesh M (2014) A new method for simultaneous calibration of demand pattern and Hazen-Williams coefficients in water distribution systems. Water Resour Manag 28(7):2021–2034. doi:10.1007/s11269-014-0592-4

    Article  Google Scholar 

  • Do NC, Simpson AR, Deuerlein JW, Piller O (2016) Calibration of water demand multipliers in water distribution systems using Genetic Algorithms. Journal of water Resources Planning and Management, ASCE, 04016044-1-04016044-13. doi:10.1061/(ASCE)WR.1943-5452.0000691

  • Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and Resources. Evolutionary Computation – IEEE 1:101–106. doi:10.1109/CEC.2001.934374

    Google Scholar 

  • Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping Algorithm. Journal of Water Resources Planning and Management, ASCE 129(3):210–225. doi:10.1061/(ASCE)0733-9496(2003)129:3(210)

    Article  Google Scholar 

  • Haghihi A, Ramos HM (2012) Detection of leakage freshwater and friction factor calibration in drinking networks using central force optimization. Water Resour Manag 26(8):2347–2363. doi:10.1007/s11269-012-0020-6

    Article  Google Scholar 

  • Jung BS, Karney BW (2008) Systematic exploration of pipeline network calibration using transients. J Hydraul Res 46(1):129–137. doi:10.1080/00221686.2008.9521947

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. Evolutionary Computation evolutionary Computation – IEEE 1942–1948.

  • Lamont PA (1981) Common pipe flow formulas compared with the theory of roughness. American Water Works Association 73(5):274–280

    Google Scholar 

  • Liberatore S, Sechi GM (2009) Location and calibration of valves in water distribution networks using a scatter-search meta-heuristic approach. Water Resour Manag 23(8):1479–1495. doi:10.1007/s11269-008-9337-6

    Article  Google Scholar 

  • Maier HE, Dandy GC (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resour Res 32(4):1013–1022. doi:10.1029/96WR03529

    Article  Google Scholar 

  • Møller MF, (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural networks, 6(4): 525–533

  • Mora-Melia D, Iglesias-Rey PL, Martinez-Solano FJ, Ballesteros-Pérez P (2015) Efficiency of evolutionary Algorithms in water network pipe sizing. Water Resour Manag 29(13):4817–4831. doi:10.1007/s11269-015-1092-x

    Article  Google Scholar 

  • Mounce SR, Mounce RB, Jackson T, Austin J, Boxall JB (2014) Pattern matching and associative artificial neural networks for water distribution system time series data analysis. J Hydroinf 16(3):617–632. doi:10.2166/hydro.2013.057

    Article  Google Scholar 

  • Msiza IS, Nelwamondo FV, Marwala T (2008) Water demand prediction using artificial neural networks and support vector regression. Journal of Computers 3(11):1–8. doi:10.4304/jcp.3.11.1-8

    Article  Google Scholar 

  • Nazif S, Karamouz M, Tabesh M, Moridi A (2010) Pressure Management model for urban water distribution networks. Water Resour Manag 24(3):437–458. doi:10.1007/s11269-009-9454-x

    Article  Google Scholar 

  • Ormsbee LE (1989) Implicit network calibration. Journal of Water Resources Planning and Management, ASCE 115(2):243–257. doi:10.1061/(ASCE)0733-9496(1989)115:2(243)

    Article  Google Scholar 

  • Ostfeld A, Salomons E, Lindell O et al (2012) Battle of the water calibration networks. Journal of Water Resources Planning and Management, ASCE 138(5):523–532. doi:10.1061/(ASCE)WR.1943-5452.0000191

    Article  Google Scholar 

  • Puccini GD, Blaser LE, Bonetti CA, Butarelli A (2016) Robustness-based design of water distribution networks. Water Utility Journal 13:13–28

    Google Scholar 

  • Roma J, Pérez R, Sanz G, Grau S (2015) Model calibration and leakage assessment applied to a real water distribution network. In: 13th Computer control for water Industry Conference. Leicester, England, September 119:603–612

    Google Scholar 

  • Rossman AL (2000) EPANET 2.0 User's manual. Drinking water research division, risk reduction Engineering Laboratory, U.S. Environmental Protection Agency

  • Seyoum AG, Tanyimboh TT, Siew C (2016) Practical application of penalty-free evolutionary multi-objective optimization of water distribution systems. Water Utility Journal 12:49–55

    Google Scholar 

  • Sunela MI, Puust R (2015) Real time water supply system hydraulic and quality modeling – a case study". In: 13th Computer control for water Industry Conference, Leicester, England, September, 119: 744–752.

  • Tsakiris G, Spiliotis M (2012) Applying resilience indices for assessing the reliability of water distribution systems. Water Utility Journal 3:19–27

    Google Scholar 

  • Vitkovsky J, Liggett J, Simpson A, Lambert M (2003) Optimal measurement site locations for inverse transient analysis in pipe networks. Journal of Water Resources Planning and Management, ASCE 129(6):480–492. doi:10.1061/(ASCE)0733-9496(2003)129:6(480)

    Article  Google Scholar 

  • Walski TM (1986) Case study: pipe network model calibration issues. Journal of Water Resources Planning and Management, ASCE 112(2):238–249. doi:10.1061/(ASCE)0733-9496(1986)112:2(238)

    Article  Google Scholar 

  • Walski TM, DeFrank N, Voglino T, Wood R, Whitman B (2006) Determining the accuracy of automated calibration of pipe network models. In: 8th annual water distribution systems analysis symposium, Cincinnati, Ohio, USA, august.

  • Water Research Centre (1989) Network analysis – a code of practive. Published by WRc, Swindon, England

    Google Scholar 

  • Yu Z, Tian Y, Zheng Y, Zhai X (2009) Calibration of pipe roughness coefficient based on manning formula and Genetic Algorithm. Transactions of Tianjin University - Springer 15:452–456. doi:10.1007/s12209-009-0078-2

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Civil Engineering, Architecture and Urbanism, Campinas State University, 951 Albert Einstein Avenue, University City Zeferino Vaz, Campinas, SP, 13.083-189, Brazil

    Gustavo Meirelles, Daniel Manzi, Bruno Brentan & Thaisa Goulart

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  1. Gustavo Meirelles
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  2. Daniel Manzi
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  3. Bruno Brentan
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  4. Thaisa Goulart
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  5. Edevar Luvizotto Jr
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Correspondence to Bruno Brentan.

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Meirelles, G., Manzi, D., Brentan, B. et al. Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resour Manage 31, 4339–4351 (2017). https://doi.org/10.1007/s11269-017-1750-2

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