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Comparing Physics-Based, Conceptual and Machine-Learning Models to Predict Groundwater Levels by BMA.

Ground water·2025
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Robust Data Worth Analysis with Surrogate Models.

Moritz Gosses1, Thomas Wöhling1

  • 1Institute for Hydrology, Technische Universität Dresden, 01069, Dresden, Germany.

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Summary
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Faster surrogate models and data worth analysis improve uncertainty quantification in groundwater modeling. A proper orthogonal decomposition (POD) surrogate accurately estimates data worth, unlike simpler models, for better predictions.

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Area of Science:

  • Environmental science
  • Hydrogeology
  • Computational modeling

Background:

  • Physically based groundwater models are computationally demanding for uncertainty analysis.
  • Existing methods struggle with high computational costs for predicting system states under unknown conditions.

Purpose of the Study:

  • To develop and evaluate faster surrogate models for uncertainty analysis in groundwater modeling.
  • To assess the data worth of existing, additional, and parametric data using robust methods.
  • To compare the performance of different surrogate models against a complex benchmark model.

Main Methods:

  • Utilized a structurally and parametrically simplified model and a proper orthogonal decomposition (POD) surrogate.
  • Combined first-order second-moment uncertainty quantification with null-space Monte Carlo techniques.
  • Compared data worth estimations from surrogates with a complex MODFLOW benchmark model.

Main Results:

  • Both surrogates showed good agreement with the benchmark for estimating the worth of existing data.
  • The POD surrogate accurately reproduced the worth of additional and parametric data, unlike the simplified model.
  • The POD model's performance suggests the necessity of accounting for parameter non-uniqueness.

Conclusions:

  • POD surrogate models offer a computationally efficient and accurate approach for data worth analysis in groundwater modeling.
  • Accounting for parameter non-uniqueness is crucial for robust uncertainty quantification.
  • Surrogate models can significantly reduce the computational burden of uncertainty analysis in complex hydrological systems.