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Battle of the water calibration networks

  • Avi Ostfeld*
  • , Elad Salomons
  • , Lindell Ormsbee
  • , James G. Uber
  • , Christopher M. Bros
  • , Paul Kalungi
  • , Richard Burd
  • , Boguslawa Zazula-Coetzee
  • , Teddy Belrain
  • , Doosun Kang
  • , Kevin Lansey
  • , Hailiang Shen
  • , Edward McBean
  • , Zheng Yi Wu
  • , Tom Walski
  • , Stefano Alvisi
  • , Marco Franchini
  • , Joshua P. Johnson
  • , Santosh R. Ghimire
  • , Brian D. Barkdoll
  • Tiit Koppel, Anatoli Vassiljev, Joong Hoon Kim, Gunhui Chung, Do Guen Yoo, Kegong Diao, Yuwen Zhou, Ji Li, Zilong Liu, Kui Chang, Jinliang Gao, Shaojian Qu, Yixing Yuan, T. Devi Prasad, Daniele Laucelli, Lydia S. Lyroudia Vamvakeridou, Zoran Kapelan, Dragan Savic, Luigi Berardi, Giuseppe Barbaro, Orazio Giustolisi, Masoud Asadzadeh, Bryan A. Tolson, Robert McKillop
*Corresponding author for this work
  • Technion-Israel Institute of Technology
  • not available
  • University of Kentucky
  • University of Cincinnati
  • MWH
  • not available
  • University of Arizona
  • University of Guelph
  • Bentley Systems, Inc.
  • University of Ferrara
  • Michigan Technological University
  • University of Georgia
  • Tallinn University of Technology
  • Korea University
  • Korean Institute of Civil Engineering and Building Technology
  • Beijing University of Technology
  • Harbin Institute of Technology
  • University of Salford
  • Polytechnic University of Bari
  • University of Exeter
  • University Institute of Architecture of Reggio Calabria
  • University of Waterloo

Research output: Contribution to journalArticlepeer-review

Abstract

Calibration is a process of comparing model results with field data and making the appropriate adjustments so that both results agree. Calibration methods can involve formal optimization methods or manual methods in which the modeler informally examines alternative model parameters. The development of a calibration framework typically involves the following: (1) definition of the model variables, coefficients, and equations; (2) selection of an objective function to measure the quality of the calibration; (3) selection of the set of data to be used for the calibration process; and (4) selection of an optimization/manual scheme for altering the coefficient values in the direction of reducing the objective function. Hydraulic calibration usually involves the modification of system demands, fine-tuning the roughness values of pipes, altering pump operation characteristics, and adjusting other model attributes that affect simulation results, in particular those that have significant uncertainty associated with their values. From the previous steps, it is clear that model calibration is neither unique nor a straightforward technical task. The success of a calibration process depends on the modeler's experience and intuition, as well as on the mathematical model and procedures adopted for the calibration process. This paper provides a summary of the Battle of theWater Calibration Networks (BWCN), the goal of which was to objectively compare the solutions of different approaches to the calibration of water distribution systems through application to a real water distribution system. Fourteen teams from academia, water utilities, and private consultants participated. The BWCN outcomes were presented and assessed at the 12th Water Distribution Systems Analysis conference in Tucson, Arizona, in September 2010. This manuscript summarizes the BWCN exercise and suggests future research directions for the calibration of water distribution systems.

Original languageEnglish
Pages (from-to)523-532
Number of pages10
JournalJournal of Water Resources Planning and Management - ASCE
Volume138
Issue number5
DOIs
StatePublished - 2012

Keywords

  • Calibration
  • Model
  • Network
  • Optimization
  • Water distribution systems

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