Use of agent-based modelling in emergency management under a range of flood hazards
Bibliographic record
Abstract
The Life Safety Model (LSM) was developed some 15 years ago, originally for dam break assessments and for informing reservoir evacuation and emergency plans. Alongside other technological developments, the model has evolved into a very useful agent-based tool, with many applications for a range of hazards and receptor behaviour. HR Wallingford became involved in its use in 2006, and is now responsible for its technical development and commercialisation. Over the past 10 years the model has been applied to a range of flood hazards, including coastal surge, river flood, dam failure and tsunami, and has been verified against historical events. Commercial software licences are being used in Canada, Italy, Malaysia and Australia. A core group of LSM users and analysts has been specifying and delivering a programme of model enhancements. These include improvements to traffic behaviour at intersections, new algorithms for sheltering in high-rise buildings, and the addition of monitoring points to allow detailed analysis of vehicle and pedestrian movement. Following user feedback, the ability of LSM to handle large model ‘worlds’ and hydrodynamic meshes has been improved. Recent developments include new documentation, performance enhancements, better logging of run-time events and bug fixes. This paper describes some of the recent developments and summarises some of the case study applications, including dam failure analysis in Japan and mass evacuation simulation in England.
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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 | 0.002 | 0.003 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".