1. Auflage - Februar 2007
97,90 Euro
2007. 480 Seiten, Hardcover
ISBN-10: 0-471-77636-X
ISBN-13: 978-0-471-77636-9 - John Wiley & Sons

Preis inkl. Mehrwertsteuer zzgl. Versandkosten.





Probekapitel

Kurzbeschreibung
Software and mathematical models are used to represent complex processes and simulate lab or field conditions. Effective Groundwater Model Calibration presents a set of methods and guidelines for calibrating and analyzing mathematical groundwater models. It helps readers address societal issues related to natural and engineered systems that are conducive to quantitative modeling, and to use the models and available data more effectively.

Aus dem Inhalt
Preface.

1 Introduction.

2 Computer Software and Groundwater Management Problem Used in the Exercises.

Exercise 2.1: Simulate Steady-State Heads and Perform Preparatory Steps.

3 Comparing Observed and Simulated Values Using Objective Functions.

Exercise 3.1: Steady-State Parameter Definition.

Exercise 3.2: Observations for the Steady-State Problem.

Exercise 3.3: Evaluate Model Fit Using Starting Parameter Values.

4 Determining the Information that Observations Provide on Parameter Values using Fit-Independent Statistics.

Exercise 4.1: Sensitivity Analysis for the Steady-State Model with Starting Parameter Values.

5 Estimating Parameter Values.

Exercise 5.1: Modified Gauss-Newton Method and Application to a Two-Parameter Problem.

Exercise 5.2: Estimate the Parameters of the Steady-State Model.

6 Evaluating Model Fit.

Exercise 6.1: Statistical Measures of Overall Fit.

Exercise 6.2: Evaluate Graph Model fit and Related Statistics.

7 Evaluating Estimated Parameter Values and Parameter Uncertainty.

Exercise 7.1: Parameter Statistics.

Exercise 7.2: Consider All the Different Correlation Coefficients Presented.

Exercise 7.3: Test for Linearity.

8 Evaluating Model Predictions, Data Needs, and Prediction Uncertainty.

Exercise 8.1: Predict Advective Transport and Perform Sensitivity Analysis.

Exercise 8.2: Prediction Uncertainty Measured Using Inferential Statistics.

9 Calibrating Transient and Transport Models and Recalibrating Existing Models.

Exercises 9.1 and 9.2: Simulate Transient Hydraulic Heads and Perform Preparatory Steps.

Exercise 9.3: Transient Parameter Definition.

Exercise 9.4: Observations for the Transient Problem.

Exercise 9.5: Evaluate Transient Model Fit Using Starting Parameter Values.

Exercise 9.6: Sensitivity Analysis for the Initial Model.

Exercise 9.7: Estimate Parameters for the Transient System by Nonlinear Regression.

Exercise 9.8: Evaluate Measures of Model Fit.

Exercise 9.9: Perform Graphical Analyses of Model Fit and Evaluate Related Statistics.

Exercise 9.10: Evaluate Estimated Parameters.

Exercise 9.11: Test for Linearity.

Exercise 9.12: Predictions.

10 Guidelines for Effective Modeling.

11 Guidelines 1 Through 8--Model Development.

Guideline 1: Apply the Principle of Parsimony.

Guideline 2: Use a Broad Range of System Information to Constrain the Problem.

Guideline 3: Maintain a Well-Posed, Comprehensive Regression Problem.

Guideline 4: Include Many Kinds of Data as Observations in the Regression.

Guideline 5: Use Prior Information Carefully.

Guideline 6: Assign Weights that Reflect Errors.

Guideline 7: Encourage Convergence by Making the Model More Accurate and Evaluating the Observations.

Guideline 8: Consider Alternative Models.

12 Guidelines 9 and 10--Model Testing.

Guideline 9: Evaluate Model Fit.

Guideline 10: Evaluate Optimized Parameter Values.

13 Guidelines 11 and 12--Potential New Data.

Guideline 11: Identify New Data to Improve Simulated Processes, Features, and Properties.

Guideline 12: Identify New Data to Improve Predictions.

14 Guidelines 13 and 14--Prediction Uncertainty.

Guideline 13: Evaluate Prediction Uncertainty and Accuracy Using Deterministic Methods.

Guideline 14: Quantify Prediction Uncertainty Using Statistical Methods.

15 Using and Testing the Methods and Guidelines.

Appendix A: Objective Function Issues.

Appendix B: Calculation Details of the Modified Gauss-Newton Method.

Appendix C: Two Important Properties of Linear Regression and the Effects of Nonlinearity.

Appendix D: Selected Statistical Tables.

References.

Index.