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Abstract
Calibration of highly‐parameterized numerical models typically requires explicit Tikhonovtype regularization to stabilize the inversion process. This regularization can take the form of a preferred parameter values scheme or preferred relations between parameters, such as the preferred equality scheme. The resulting parameter distributions calibrate the model to a user‐defined acceptable level of model‐to‐measurement misfit, and also minimize regularization penalties on the total objective function. To evaluate the potential impact of these two regularization schemes on model predictive ability, a dataset generated from a synthetic model was used to calibrate a highly-parameterized variable‐density SEAWAT model. The key prediction is the length of time a synthetic pumping well will produce potable water. A bi‐objective Pareto analysis was used to explicitly characterize the relation between two competing objective function components: measurement error and regularization error. Results of the Pareto analysis indicate that both types of regularization schemes affect the predictive ability of the calibrated model.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Evaluating the effect of Tikhonov regularization schemes on predictions in a variable-density groundwater model |
| Year Published | 2010 |
| Language | English |
| Publisher | Wechselnde Verlagsorte |
| Contributing office(s) | Florida Water Science Center-Ft. Lauderdale |
| Description | 5 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Conference publication |
| Larger Work Title | SWIM21 – 21st Salt Water Intrusion Meeting Proceedings Book |
| First page | 344 |
| Last page | 348 |
| Conference Title | 21st Salt Water Intrusion Meeting (SWIM21 – AZORES 2010) |
| Conference Location | Azores, Portugal |
| Conference Date | June 21-26, 2010 |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |