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1、1Dr. John Doherty (author of PEST) and Queensland Department of Natural Resources present a short course on:Model Calibration and Predictive Analysis using PEST 2Model Calibration and Predictive Analysis using PESTIntroduction This intensive short course will instruct participants on the automated c
2、alibration of environmental models, and on the analysis of the predictive uncertainty associated with such models. The principal instructor is the developer of PEST, the industry standard software package for model-independent, automated calibration and predictive uncertainty analysis. What you will
3、 learn While the course will include a thorough coverage of the theory and applications of nonlinear parameter estimation techniques in the calibration of different types of models, there will also be a strong practical aspect of the course. Participants will gain hands-on experience in the use of P
4、EST-ASP (the latest version of PEST), including its advanced regularisation and predictive analysis features. Experience will be gained in the use of these features in the calibration of groundwater flow and transport models, surface water quality and quantity models, as well as other types of model
5、s including a vadose zone model and a biological growth model. Topics covered will include:- theory of nonlinear parameter estimation, application of nonlinear parameter estimation to model calibration, analysis of uncertainty and nonuniqueness in calibrated parameters, problem regularisation, analy
6、tical and intuitive analysis of calibration residuals, the effects of parameter uncertainty on model predictive uncertainty, simultaneous calibration of multiple models, use of PESTs predictive analyser, use of PEST in regularisation mode, use of different methods of spatial parameterisation, pilot
7、points in calibration of groundwater models, combining stochastic field generation with nonlinear parameter estimation, how to choose an appropriate level of model complexity.In the practical sessions, participants will gain hands-on experience in using all aspects of PEST functionality with a numbe
8、r of different models, including MODFLOW, MT3DMS (extremely popular public domain groundwater flow and transport models), SWIM (a Richard-equation-based, unsaturated zone, water-movement model), 3PG (a forest production model) and HSPF (a much-used USEPA/USGS model for simulation of non-urban, non-p
9、oint pollution of surface water systems). Participants will also be introduced to a suite of utility programs which automate PEST set-up for these (and similar) models.If he/she wishes, each participant can bring his/her own case study as a substitute for the practical exercises provided with the co
10、urse. If time permits, customized PEST applications for these case studies can be developed under the guidance of the course instructor. (Note that the model must have ASCII input and output files and run in the command-line environment. Bring the model executable and all your case files, on your ow
11、n laptop if you wish.) What is Nonlinear Parameter Estimation? In simple terms, nonlinear parameter estimation is a methodology whereby the arduous, labour-intensive and distinctly frustrating task of multi-parameter model calibration can be carried out automatically under the control of a computer.
12、 The advantages of computer-aided model calibration include the following:- the task is accomplished much more rapidly than by using manual methods; better parameter estimates are obtained;3estimates of the uncertainties accompanying optimised parameters are produced as part of the calibration proce
13、ss; freed of the drudgery of having to undertake countless model runs “by hand”, the modeller is able to inject more initiative into the calibration process, thus making this process a partnership between calibration technology and the art of the modeller; a greater understanding of the environmenta
14、l processes simulated by the model can be gained through viewing model calibration as a sophisticated method of data interpretation; use of advanced regularisation techniques to define a “default system condition” can allow environmental data interpretation to take place in a context of intrinsic pa
15、rameter reasonableness; an analysis of the effects of parameter uncertainty on model predictive uncertainty can be undertaken.What is PEST? PEST is a model-independent nonlinear parameter estimator. Since its inception seven years ago, PEST has become the industry standard in calibration of environm
16、ental models of all kinds. The two cornerstones of PESTs model-independence are:- PEST communicates with a model through the models own input and output files. Hence the model does not need to be re-compiled to be linked to PEST; it can be used with PEST exactly as it is. Though based on the Gauss-Marquardt-Levenberg method, the nonlinear parameter estimation algorithm used by PEST is uniquely robust and powerful, having been developed
