Decision Analysis for Robust Long-Term Flood Management: Uncertainty Exploration Using Probabilistic Approach and Information-Gap Decision Theory
Bibliographic record
Abstract
Decision making for flood risk management involves comparing options based on their benefits and costs.These choices always involve considerable uncertainties, especially when long-term projections are being developed, taking climate change into account.The aim of the study is to reveal what is the uncertainty robustness of alternative flood defense measures.The treatment of different sources of uncertainty is carried out by using probabilistic analysis of net present value (NPV) as well as using information gap decision theory (IGDT).The focal point of the study is a settlement in Nord Bulgaria with a record of severe flooding in the past, for which divergent climate change projections have been generated under the RCP 4.5 and RCP 8.5 scenarios.The behavior of three civil defense options under these uncertainty conditions is explored over an extended 30-year time horizon up to 2050.The paper shows sequentially Probabilistic Performance Analysis with NPV performance criteria, and then how Information-Gap Decision Theory can be formulated and used to analyze same options.After discussing the results, we conclude that facing deep uncertainty in long-term flood protection decision making, it is advisable to use multiple methods that differ in data and assumptions, necessarily taking into account hydrological uncertainty from climate change, that could dramatically change our choices.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".