Search

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 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. [ABSTRACT FROM AUTHOR]

A multi-stage response procedure for identifying possible ingress nodes (PINs) and quantifying the likelihood that a PIN in a given water distribution system is the actual point of ingress is described. The procedure uses data mining to successively decrease the number of PINs based on a pre-constructed database. In each stage, query sentences are executed to locate the PINs and a Euclidean distance is proposed to estimate the probability, to allow the identification of locations with the highest probabilities of being the true ingress location. As demonstrated in a case study, the ranges of PINs are reduced in the 1st, 2nd, and 3rd stages; except the first sensor alarm, the Euclidean distance metric can identify the true ingress node with the program run-time of less than 2 min; the multi-stage procedure saves roughly 3 h in identifying the true ingress node after the second sensor alarm, instead of waiting for a third sensor alarm to provide the location information. The multi-stage response procedure is shown to be an effective and efficient way for identification and probability quantification of PINs. [ABSTRACT FROM AUTHOR]