Development and application of efficient methods for the forward propagation of epistemic uncertainty and sensitivity analysis within complex broad-scale flood risk system modelsThis article is one of a selection of papers published in this Special Issue on Hydrotechnical Engineering.
Bibliographic record
Abstract
Increasingly, an understanding of flood risk across regions and nations, and an ability to explore how these might change in time, is seen as a prerequisite to effective and efficient flood risk management. In response, specific flood risk analysis methods have been developed that are both accurate and fast to run. Although widely acknowledged as desirable, it has not previously been possible to quantify the uncertainty associated with the assessed flood probability, consequence, or risk. To help overcome this deficiency, an efficient method for the propagation of epistemic uncertainties through large-scale flood risk system models has been developed and trialed for three pilot catchments. The approach is allied to an efficient sensitivity analysis that enables the influence of individual uncertainties on the output quantity of risk to be isolated, enabling future research, development, and data-gathering efforts to be focused.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".