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Visual PEST-ASP

Detailed Description

Visual PEST-ASP

Visual PEST-ASP





INTRODUCTION TO Visual PEST-ASP

Visual PEST combines the powerful parameter estimation capabilities of PEST-ASP with the graphical processing and display features of WinPEST.

ABOUT PEST

PEST-ASP is the latest version of PEST, the pioneer in model-independent parameter estimation. Since it was first released over six years ago, PEST has gained extensive use all over the world in many different fields. Over this time it has undergone continued development with the addition of many new features that have improved its performance and utility to a level that makes it uniquely applicable in just about any modeling environment. PEST is now used extensively for automated model calibration and data interpretation in groundwater and surface water hydrology, geophysics, geotechnical, mechanical and mining engineering, as well as many other fields.

About WinPEST

WinPEST brings parameter estimation and predictive analysis to life! Not only can you run PEST in a friendly, intuitive Windows environment, you can study a set of comprehensive, evolving run-time displays of key PEST variables providing a wealth of information on the progress of the parameter estimation process being undertaken by PEST. When PEST has finished execution, an additional suite of plots become available displaying a full range of statistical and other information on calibrated parameters, calibration residuals and other PEST variables.

You will be amazed at what you can learn about your model, the information content of your calibration dataset, and the confidence you can place in model results from an examination of WinPEST's high-impact plots. You will truly wonder how you ever got along without it!


Visual PEST-ASP MAIN FEATURES

Here are just some of the features which make Visual PEST truly unique.

Inversion Engine
PEST uses an extremely robust numerical inverse-problem-solver with painstaking attention to detail in all aspects of design and implementation.

Parameter Bounds
The user is able to set upper and lower bounds on parameters during the calibration process, thus ensuring that estimated values are in range.

User Intervention
PEST allows user-intervention in the parameter estimation process whereby troublesome parameters can be held for a while and the most recent parameter upgrade repeated without the need for extensive recalculation.

Parallel Processing
With Parallel PEST, model runs can be distributed across a PC network. Savings in overall optimization time can be enormous.

Utilities
PEST is accompanied by a suite of powerful utilities which automate PEST setup and carry out extensive error checking on all aspects of PEST input dataset construction.

Data Handling
Observations and parameters can be grouped for ease of property definition and weights allocation, and to add flexibility to the inversion process.

Predictive Analysis
PEST's unique predictive analyzer allows the user to estimate the true range of predictive nonuniqueness associated with a calibrated model.

WinPEST
The WinPEST graphical user interface displays an extensive range of run-time and post-run data for easy analysis and postprocessing.

Experience
PEST has stood the test of time. It has been used to carry out countless calibration and data interpretation exercises in all fields of science and engineering. Over its six-year life it has undergone continuous improvement and refinement to keep it at the cutting edge of model-calibration technology.


Visual PEST-ASP MODEL INDEPENDENCE

PEST brings parameter estimation technology to all modelers by combining a powerful inversion engine with the ability to communicate with a model through the model's own input and output files. Thus PEST can be used with any model without the need for any programming. To calibrate a model, simply:

  • inform PEST which numbers to adjust on model input files, 
  • identify those numbers on model output files for which there are corresponding field or laboratory measurements, and 
  • instruct PEST how to run the model (assumed to be command-line-driven).

PEST then takes control of the model, running it as many times as it needs to while adjusting parameter values until the discrepancies between model outputs and corresponding field or laboratory measurements are as small as possible in the weighted least squares sense. It then lists optimal parameter values, an estimate of the uncertainty associated with optimal parameter values, best-fit model outcomes, model-to-measurement residuals, and a suite of statistics related to the optimal parameter and residual sets.

Visual PEST MODEL CALIBRATION & DATA INTERPRETATION

With PEST you can turn any model into a powerful data interpretation package. Model calibration is no longer the time-consuming, frustrating and often fruitless exercise that it used to be. The user is free to unleash his/her creativity in the calibration and data-interpretation process while PEST carries out the numerically intensive calculations required to implement his/her ideas. PEST allows a modeler to truly understand the capacity that a dataset possesses for the estimation of parameters governing the workings of a system, and how supplementary data are most efficiently gathered in order to increase that capacity.

The possibilities for creativity and elegance in data interpretation and model calibration are truly enormous with PEST. The "model" can be a batch file holding one or many executables. Thus you can calibrate a model using data gathered over noncontiguous time intervals; you can undertake simultaneous calibration of a steady-state and transient model, a flow and transport model, multiple recharge models together with a flow model, a flow model combined with regularization functionality, and much, much more. Because there is no limit to the number of model input files that PEST can write and the number of model output files which PEST can read, the possibilities for composite model construction are limited only by a user's imagination.

Visual PEST PREDICTIVE ANALYSIS

PEST-ASP introduces predictive analysis, the latest development in parameter estimation technology. The Predictive Analyzer is a revolutionary approach to modeling that allows the modeler to actually calculate the uncertainties in model predictions arising from uncertainties in model parameters while ensuring the model remains calibrated.

A common mistake in many modeling exercises is to undertake "sensitivity analysis" after a model has been calibrated in order to estimate the uncertainties in model predictions. There are two problems with this approach. The first is that when a parameter is varied in order to test the effects of this variation on predictive output, the model may become uncalibrated. Thus the prediction cannot be considered a true model prediction. The second problem is that the variation of individual parameters by a small amount in order to assess predictive uncertainty, may seriously underestimate the extent to which parameters could actually vary and still keep the model calibrated; the trick is to vary not just one, but possibly many correlated parameters together in such a way that the variation of these parameters has virtually no effect on model outcomes under calibration conditions. It is the variation of this combination of parameters (rather than each parameter individually) that must be undertaken to perform true predictive analysis.

The unique PEST-ASP predictive analyzer allows the modeler to vary parameters in such a way as to ensure that the model remains calibrated while, at the same time, testing the effect of this variation on key model predictions. The modeler can ask PEST to calculate the highest or lowest value of a key model outcome while at the same time ensuring that the parameter values used to make this prediction are such as to keep the model calibrated. The repercussions for model deployment are profound. Now the user can test best and worst case scenarios with ease, design a fail-safe remediation system and/or optimize the efficiency of a monitoring network. Modeling will never be the same!

Predictive Analysis allows you to quantify the uncertainties typically associated with modeling by directly calculating the definitive uncertainty limits on key model predictions.


Visual PEST PARALLEL PROCESSING

PEST-ASP comes with sophisticated parallel processing capabilities enabling it to distribute and manage model runs across a network to significantly reduce optimization times. Thus model calibration or predictive analysis can now be undertaken using more parameters and larger models than has hitherto been possible.

WinPEST

WinPEST's high impact and informative graphics allows you to understand the calibration and predictive analyses processes like never before. Through a series of evolving run-time displays, you can tell at a glance where the process is going and whether or not your intervention may be required (see below). When PEST has finished running, WinPEST presents a further array of colorful and educational plots through which you can examine parameter uncertainty and nonuniqueness, analyze calibration residuals (either as a whole or in user-defined groups), and much more besides.

WinPEST can be used interchangeable with PEST-ASP (and previous versions of PEST) so you can import your existing PEST datasets and give them a whole new lease of life as they explode into color.

WinPEST USER INTERVENTION

Sometimes the model calibration or predictive analysis process encounters numerical difficulties. If these are hampering a PEST run, WinPEST's informative displays not only make this plain but provide information through which troublesome parameters (normally insensitive and/or highly correlated parameters) can be identified. With a few mouse clicks, you can then halt PEST execution, hold the offending parameters at their current values, and recalculate improvements to the other parameters without having to recompute the Jacobian matrix (the most time-consuming aspect of the parameter estimation process). Using this unique methodology you can, more often than not, get a stalled calibration process "back on the rails" with minimum wasting of computer time - often a big issue with large and complex models.


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