Development of an agent-based model to improve emergency planning for floods and dam failures
Bibliographic record
Abstract
Abstract The Life Safety Model (LSM) is an agent-based model which assists with emergency planning and risk assessments for floods and dam failures by providing estimates of fatalities and evacuation times. The LSM represents the interactions of agents (i.e. people, vehicles, and buildings) with the floodwater. The LSM helps to increase the accuracy of estimates of loss of life and evacuation times for these events by taking into account a number of parameters which are not described in empirical models, such as the people's characteristics (e.g. age and gender), building construction types, and the road network. The LSM has been applied to three historic flood-related disasters: the 1953 coastal floods, in the UK; the 1959 Malpasset Dam failure, in France; the 2019 Brumadinho tailings dam disaster, in Brazil. These illustrate how the LSM has been verified and improvements to evacuation routes, early warnings, and the refuge locations could have reduced the number of fatalities. The value of using the LSM is not to calculate the ‘exact’ number of flood deaths or evacuation times, but to assess if emergency management interventions can significantly reduce them. The LSM can also be used to assess whether the societal risk posed by dams and flood defences is ‘acceptable’.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".