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contributor authorAvi Ostfeld
contributor authorElad Salomons
contributor authorLindell Ormsbee
contributor authorJames G. Uber
contributor authorChristopher M. Bros
contributor authorPaul Kalungi
contributor authorRichard Burd
contributor authorBoguslawa Zazula-Coetzee
contributor authorTeddy Belrain
contributor authorDoosun Kang
contributor authorKevin Lansey
contributor authorHailiang Shen
contributor authorEdward McBean
contributor authorZheng Yi Wu
contributor authorTom Walski
contributor authorStefano Alvisi
contributor authorMarco Franchini
contributor authorJoshua P. Johnson
contributor authorSantosh R. Ghimire
contributor authorBrian D. Barkdoll
contributor authorTiit Koppel
contributor authorAnatoli Vassiljev
contributor authorJoong Hoon Kim
contributor authorGunhui Chung
contributor authorDo Guen Yoo
contributor authorKegong Diao
contributor authorYuwen Zhou
contributor authorJi Li
contributor authorZilong Liu
contributor authorKui Chang
contributor authorJinliang Gao
contributor authorShaojian Qu
contributor authorYixing Yuan
contributor authorT. Devi Prasad
contributor authorDaniele Laucelli
contributor authorLydia S. Vamvakeridou Lyroudia
contributor authorZoran Kapelan
contributor authorDragan Savic
contributor authorLuigi Berardi
contributor authorGiuseppe Barbaro
contributor authorOrazio Giustolisi
contributor authorMasoud Asadzadeh
contributor authorBryan A. Tolson
contributor authorRobert McKillop
date accessioned2017-05-08T22:03:24Z
date available2017-05-08T22:03:24Z
date copyrightSeptember 2012
date issued2012
identifier other%28asce%29wr%2E1943-5452%2E0000235.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/70048
description abstractCalibration 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 the Water 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.
publisherAmerican Society of Civil Engineers
titleBattle of the Water Calibration Networks
typeJournal Paper
journal volume138
journal issue5
journal titleJournal of Water Resources Planning and Management
identifier doi10.1061/(ASCE)WR.1943-5452.0000191
treeJournal of Water Resources Planning and Management:;2012:;Volume ( 138 ):;issue: 005
contenttypeFulltext


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