Multi‐Criteria Decision Support Systems for Flood Hazard Mitigation and Emergency Response in Urban Watersheds<sup>1</sup>
Bibliographic record
Abstract
Abstract: Flood management problems are inherently complex, time‐bound and multi‐faceted, involving many decision makers (with conflicting priorities and dynamic preferences), high decision stakes, limited technical information (both in terms of quality and quantity), and difficult tradeoffs. Multi‐Criteria Decision Support Systems (MCDSS) can help to manage this complexity and decision load by combining value judgments and technical information in a structured decision framework. A brief overview of MCDSS is presented, an original MCDSS architecture is put forth, and future research directions are discussed, including extensions to Multi‐Criteria Spatial Decision Support Systems and group MCDSS (as flood management involves shared resources and broad constituencies). With application to the September 11‐12, 2000 Tokai floods in Japan, the proposed multi‐criteria decision support instruments enhance communication among stakeholders and improve emergency management resource allocation. In summary, by making the links among flood knowledge, assumptions and choices more explicit, MCDSS increases stakeholder satisfaction, saves lives, and reduces flood management costs, thereby increasing decision‐making effectiveness, efficiency and transparency.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".