Automated parameter optimization of a water distribution
system
Maikel Méndez, José A. Araya and Luís D. Sánchez
ABSTRACT
The hydraulic model EPANET was applied and calibrated for the water distribution system (WDS) of La
Sirena, Colombia. The Parameter ESTimator (PEST) was used for parameter optimization and
sensitivity analysis. Observation data included levels at water storage tanks and pressures at
monitoring nodes. Adjustable parameters were grouped into different classes according to two
different scenarios identified as constrained and unconstrained. These scenarios were established to
evaluate the effect of parameter space size and compensating errors over the calibration process.
Results from the unconstrained scenario, where 723 adjustable parameters were declared, showed
that considerable compensating errors are introduced into the optimization process if all parameters
were open to adjustment. The constrained scenario on the other hand, represented a more properly
discretized scheme as parameters were grouped into classes of similar characteristics and
insensitive parameters were fixed. This had a profound impact on the parameter space as adjustable
parameters were reduced to 24. The constrained solution, even when it is valid only for the system’s
normal operating conditions, clearly demonstrates that Parallel PEST (PPEST) has the potential to be
used in the calibration of WDS models. Nevertheless, further investigation is needed to determine
PPEST’s performance in complex WDS models.
Maikel Méndez (corresponding author)
José A. Araya
Centro de Investigaciones en Vivienda y
Construcción (CIVCO), Escuela de Ingeniería en
Construcción,
Instituto Tecnológico de Costa Rica,
APDO 159-7050,
Cartago,
Costa Rica
E-mail: mamendez@itcr.ac.cr
Luís D. Sánchez
Instituto Cinara,
Universidad del Valle - Facultad de Ingeniería,
Cali,
Colombia
Key words | calibration, EPANET, model, optimization, parameter, sensitivity
INTRODUCTION
Water distribution system (WDS) models can be used for a var-
iety of purposes including design, management, maintenance,
planning and scenario studies. Nevertheless, in order to be
reliable, a model must adequately predict the behavior of the
actual system under a wide range of conditions and for an
extended period of time (Machell et al. ). This can be
accomplished by calibrating the model using a set of field
measurements or observations, mainly water storage tank
(WST) levels, nodal pressures and flow rates. The calibration
of a WDS model is carried out by optimizing the values of phys-
ical and conceptual parameters involved in the model,
including pipe roughness-coefficients, minor losses, demand
pattern factors, nodal demands, control valves and pump
characteristics (USEPA ; Koppel & Vassiljev ).
Calibration is also referred to as an inverse problem,
since observed values are quantitatively compared to
model predictions until the optimum set of parameters is
found (Gallagher & Doherty ). A model is considered
to be calibrated for one set of operating conditions if it
can predict outcomes with reasonable agreement. Neverthe-
less, this does not necessarily imply calibration in general.
Models should be calibrated over a wide range of operating
conditions so that the modeler can rely on model predic-
tions (Walski ). As models are only approximations of
the actual systems that are being represented, the reliability
of model predictions depends on how well the model struc-
ture is defined and how well the model is parameterized
(Hogue et al. ).
A critical step in model calibration relates to the quality
and density of observation data which may contain
significant measurement errors. Even small measurement
errors can lead to large errors in estimated parameters
71 © IWA Publishing 2013 Journal of Hydroinformatics | 15.1 | 2013
doi: 10.2166/hydro.2012.028
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