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Related Experiment Videos

Testing alternative ground water models using cross-validation and other methods.

L Foglia1, S W Mehl, M C Hill

  • 1Ifu, ETH, Zurich, Switzerland. lfogila@ucdavis.edu

Ground Water
|September 1, 2007
PubMed
Summary
This summary is machine-generated.

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Efficient information criteria effectively rank groundwater models, while parameter correlation coefficient identifies key observations. These methods offer computational advantages over intensive cross-validation for model discrimination and sensitivity analysis.

Area of Science:

  • Hydrogeology
  • Computational modeling
  • Environmental science

Background:

  • Evaluating alternative groundwater models is crucial for accurate predictions and resource management.
  • Model discrimination and sensitivity analysis are key techniques for assessing model performance and identifying influential parameters.
  • Computational efficiency is a significant consideration for applying these methods to complex hydrological systems.

Purpose of the Study:

  • To compare the effectiveness of computationally efficient methods with intensive techniques for groundwater model discrimination and sensitivity analysis.
  • To evaluate information criteria (AICc, BIC, GCV) and sensitivity statistics (DSS, PCC, DFBETAS, Cook's D) for groundwater model assessment.
  • To introduce a novel graphical method for visualizing cross-validation and sensitivity analysis results in complex groundwater models.

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Main Methods:

  • Investigated information criteria: corrected Akaike information criterion (AICc), Bayesian information criterion (BIC), and generalized cross-validation (GCV) for model discrimination.
  • Assessed sensitivity analysis measures: dimensionless scaled sensitivity (DSS), composite scaled sensitivity, parameter correlation coefficient (PCC), DFBETAS, Cook's D, and observation-prediction statistic.
  • Employed cross-validation (CV) as a computationally intensive benchmark for both model discrimination and sensitivity analysis.
  • Applied methods to five alternative groundwater models of the Maggia Valley, focusing on hydraulic conductivity variations.

Main Results:

  • Information criteria (AICc, BIC, GCV) provided comparable model selection results to cross-validation (CV) at a significantly lower computational cost.
  • Parameter correlation coefficient (PCC) effectively identified important observations by accounting for parameter correlations, outperforming dimensionless scaled sensitivity (DSS).
  • Dimensionless scaled sensitivity (DSS) showed inferior performance in identifying important observations, likely due to its inability to incorporate parameter correlation effects.

Conclusions:

  • Computationally efficient information criteria are recommended for groundwater model selection due to their accuracy and reduced computational demands compared to CV.
  • Parameter correlation coefficient (PCC) is a valuable tool for sensitivity analysis, offering insights into observation importance that linear methods like DSS lack.
  • The developed graphical representation aids in interpreting complex cross-validation and sensitivity analysis outputs, enhancing the utility of efficient statistics for groundwater model evaluation.