Bias-corrected groundwater model prediction uncertainty analysis

Yonas Demissie, Albert J Valocchi, Barbara S Minsker, Barbara Bailey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The incomplete description of the subsurface processes by physically-based groundwater models often results in biased and correlated prediction errors, thus suggesting the need for systematic correction of errors before conducting prediction uncertainty analysis. In this work, error-mapping artificial neural networks (ANN) are used to correct the physically-based groundwater model (MODFLOW) prediction errors. The resulting prediction uncertainty of the coupled MODFLOW-ANN model is then assessed using three alternative methods. The first method establishes approximate confidence and prediction intervals using first-order least-squares regression approximation (also called first-order error analysis). The second method employs bootstrap approaches that involve resampling of the uncertain data with replacement and repeated model runs for constructing the confidence and prediction intervals. The third method relies on a Bayesian approach that uses analytical or Monte Carlo methods to derive the posterior distribution. The performance of these approaches is evaluated using a hypothetical case study developed based on a phytoremediation site at the Argonne National Laboratory, USA. The results indicate that the three approaches yield comparable confidence and prediction intervals, thus making the computationally efficient first-order error analysis approach attractive for estimating the coupled model uncertainty. The results also demonstrate that the error-mapping ANN not only captures some of the local biases in the MODFLOW prediction, but also systematically reduces the prediction variance.

Original languageEnglish (US)
Title of host publicationProceedings of an International Conference on Calibration and Reliability in Groundwater Modelling
Subtitle of host publicationCredibility of Modelling, ModelCARE2007
Pages15-21
Number of pages7
Edition320
StatePublished - Nov 7 2008
EventInternational Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007 - Copenhagen, Denmark
Duration: Sep 9 2007Sep 13 2007

Publication series

NameIAHS-AISH Publication
Number320
ISSN (Print)0144-7815

Other

OtherInternational Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007
CountryDenmark
CityCopenhagen
Period9/9/079/13/07

Keywords

  • Bias correction
  • Calibration
  • Complementary modelling
  • Uncertainty analysis

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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