This paper proposes a comprehensive and flexible framework for calibrating a hydraulic
network model. Calibration tasks can be specified for a water distribution system
according to data availability and model application requirements. It allows a user to:
flexibly choose any combination of the model parameters such as pipe roughness,
junction demand and link (pipes, valves and pumps) operational status; easily
aggregate model parameters to reduce the problem dimension for expeditious calculation;
and, consistently specify boundary conditions and junction demand loadings that are
corresponding to field data collection. A model calibration is then defined as an implicit
nonlinear optimization problem, which is solved by employing a powerful genetic
algorithm (GA), a generic search paradigm based on the principles of natural evolution
and biological reproduction. Calibration solutions are obtained by minimizing the
discrepancy between the model predicted and the field observed values of junction
pressures and pipe flows. With this methodology, a modeler can be fully assisted during a
calibration process, thus it is possible to achieve a good model calibration with high level
of confidence. As a result, calibrated models can be developed for conducting system
analysis and operational management. An example application is presented to demonstrate
the efficacy and robustness of the genetic-based methodology for calibrating a water
distribution model. Includes 11 references, figures.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 230 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 10 |
| Published : | 01/01/2002 |
