Using evacuation models to inform sustainable flood risk management policies
Bibliographic record
Abstract
The Environment Agency, Local Authorities and rescue services in England and Wales are faced with a number of challenges when managing the risk posed by the failure of flood defences and dams. Until recently, there has been little research undertaken in the UK on evacuation modelling from the perspective of improving flood risk management and informing emergency management plans. \n \nThe modelling of the evacuation process generated by an approaching flood can assist in: \n \n•Identifying the potential risks to life under dam or flood defence breach scenarios; \n•Assessing the time people have to reach safe havens; \n•Identifying potential escape bottlenecks; \n•Determining the impact of road closures due to flooding; \n•Planning and prioritising evacuation routes and safe havens for effective risk management. \n \nBeing able to model a range of evacuation scenarios can lead to the establishment of appropriate evacuation policies, strategies, and contingency plans and can help facilitate communication and information transfer. \n \nThis research have been carried out under Task 17 of the EC research project FLOODsite. The application of an evacuation and loss-of-life model, developed in Canada, to a number of heavily defended areas of the UK is described. The potential use of evacuation models in supporting the development of emergency management plans in the UK for communities living behind flood defences and downstream of large dams will also be discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".