Possibilistic response surfaces combining fuzzy targets and hydro-climatic uncertainty in flood vulnerability assessment
Bibliographic record
Abstract
Abstract. Several alternatives have been proposed to shift the paradigms of water management under uncertainty from predictive to decision-centric. An often mentioned tool is the stress-test response surface; mapping system performance to a large sample of future hydro-climatic conditions. Dividing this exposure space between success and failure requires clear performance targets. In practice, however, stakeholders and decision-makers may be confronted with ambiguous objectives for which there are no clearly-defined (crisp) performance thresholds. Furthermore, response surfaces can be non-deterministic, as they do not fully capture all possible sources of hydro-climatic uncertainty. The challenge is thus to combine two different types of uncertainty: the irreducible uncertainty of the response itself relative to the variables that describe change, and the fuzziness of the performance target. We propose possibilistic surfaces to assess flood vulnerability with fuzzy performance thresholds. Three approaches are tested and compared on a un-gridded sample of the exposure space: (i) an aggregation of logistic regressions based on α-cuts combines the uncertainty of the response itself and the ambiguity of the target within a single possibility measure; (ii) an alternative approximates the response with a fuzzy analytical surface; and (iii) a convex delineation expresses the largest range of failure specific to a given management rule without probabilistic assumptions. To illustrate the proposed approaches, we use the flood-prone reservoir system of the Upper Saint-François River Basin in Canada as a case study. This study shows that ambiguity can be effectively be considered when generating a response surface and suggests how further research could build a possibilistic framework for hydro-climatic uncertainty.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".