A Comparison of Various Uncertainty Propagation Methods: An Example of Subsurface Contaminant Transport
Bibliographic record
Abstract
Groundwater resources are under increasing threat of contamination and wasteful use in many parts of the world. Groundwater flow and integrated contaminant transport models are commonly used to predict the fate of contaminants in the subsurface environments. However, the lack of reliable data and complexity of the natural environmental systems, the predictions are subjected to large uncertainties. For reliable decision-making, these contaminant transport models are required to explicitly consider associated uncertainties in their parameters. This paper aimed to compare the results of three common uncertainty propagation methods using an example of contaminant transport in groundwater. The research employed an advection—dispersion equation (ADE) to describe the transport of a contaminant in groundwater. For simplicity, two parameters - dispersivity and velocity - were considered in the uncertainty analysis. Fuzzy set theory, Monte Carlo simulation (MCS), and Probability Box (P-Box) method were investigated. The cumulative distribution functions generated from these analyses were compared to evaluate the capabilities of these methods. The comparison showed that P-Box method provided a more comprehensive analysis with lesser assumptions as compared to other methods, and also found to be more pragmatic way to describe and propagate uncertainties in complex environmental systems. Furthermore, execution time required to perform uncertainty analysis using P-Box method was comparatively much less than MCS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".