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PEST communicates with an existing model through the model's
own input and output files.

What PEST does

PEST is a nonlinear parameter estimation package with a difference. The difference is that PEST can be used to estimate parameters for just about any existing computer model, whether or not a user has access to the model's source code. PEST is able to "take control" of a model, running it as many times as it needs to while adjusting its parameters until the discrepancies between selected model outputs and a complementary set of field or laboratory measurements is reduced to a minimum in the weighted least squares sense.

Most parameter estimation packages suffer from two serious drawbacks that inhibit their ability to optimize parameters for the plethora of computer simulation models that are used today in all fields of study. The first of these difficulties is that a model normally needs to be partially recoded in order to communicate with an estimation program; this usually involves recasting the model as a subroutine which is then called by the estimator each time it needs to run the model. The second disadvantage is that the performance of many commercial and public-domain estimators is seriously degraded when optimizing parameters for large numerical models or for the sometimes complex models used for simulating "messy" environmental processes.

PEST overcomes the first of these difficulties by communicating with a model through the model's own input and output files. Thus PEST adapts to the model; the model does not need to be adapted to PEST. It overcomes the second problem by implementing a particularly robust variant of the Gauss-Marquardt-Levenberg method of nonlinear parameter estimation. Furthermore, through adjustment of a number of control variables, a user is able to "tune" PEST's implementation of the method to suit the model for which parameters are sought.

Because PEST is model-independent, the "model" can, in fact, be a series of models which PEST runs in succession through a batch file; PEST can estimate parameters for one or all of the models simultaneously. Thus a first model can provide input data for a second model; a single model can be calibrated against a number of different historical datasets all at once; a preprocessor can be run, followed by the model, followed by a postprocessor; the possibilities are endless. The only requirements for the "model" are that it can be run from the command line that it reads and writes ASCII files, and that it can be run as a DOS or UNIX command.

Other PEST features include

• Individual model parameters can be designated as adjustable, fixed, or linked to other parameters; adjustable parameters can be log-transformed to increase optimization efficiency.
• Prior information on parameters or on relationships between parameters can be incorporated into the estimation process.
• Optimum parameter values can be constrained to lie between individually-specified upper and lower bounds using a unique procedure to enforce these constraints while simultaneously enhancing numerical stability.
• PEST execution can be interrupted at any time to inspect a detailed run record file; PEST can then be restarted exactly where it was interrupted.
• All PEST data storage is dynamically allocated; hence the problem size (including number of adjustable parameters and the size of the observation dataset) is limited only by the amount of memory installed on a user's machine.
• Composite parameter sensitivities are continuously recorded to allow easy identification of troublesome parameters.
• The user can intervene in the parameter estimation process, holding recalcitrant parameters fixed for a while; parts of the inversion process can then be repeated using previously calculated sensitivity information.
• Parameters and observations can be divided into subgroups for allocation of variables controlling calculation of model derivatives on the one hand and for assessing the contribution of various observations to the objective function on the other.
• PEST calculates statistical data for optimized parameter values including 95% confidence intervals together with the parameter covariance and correlation-coefficient matrices.
• The unique predictive analysis capabilities of PEST allow accurate determination of the effects of parameter uncertainty and predictive uncertainty.

PEST - estimating parameters for any model

PEST Categories: parameter estimation models - saturated zone parameters, parameter estimation models - unsaturated zone parameters, parameter estimation models - transport parameters, MODFLOW programs

PEST