Tools and methods to improve emergency planning for floods
Bibliographic record
Abstract
The aim of this research was to address how emergency planning for floods and dams can be improved to reduce loss of life. The thesis comprises nine publications. Each publication addresses research questions which feed into the overarching aim. Publications 1 and 2 review historic flood events which occurred in England in 1953 and in France in 2010, leading to a significant loss of life, partly because of deficiencies in emergency planning. Publications 3, 4 and 5 assess how effective emergency plans for floods can reduce fatalities. Publication 3 consolidates lessons learnt from two low-income countries, Bangladesh and Cuba, where there has been an appreciable decrease in deaths from coastal surges and how these are relevant to high-income countries such as the USA. A review of the methods and tools available to increase the effectiveness of emergency plans for floods is provided in Publication 5. Publication 6 follows on from one of the overarching findings of Publications 1 to 5, which is that there is a requirement for a method via which emergency plans for floods can be assessed and improved. Publication 6 details three cases studies in England, France and The Netherlands where a method was developed and applied to assess and to improve emergency plans for floods. Papers 7, 8 and 9 describe how an agent-based model, which represents the dynamic interaction of people, vehicles and buildings with the floodwater or mudflows, can be used to investigate different emergency planning scenarios in order to minimise the potential loss of life from different sources of flooding including dam failures. These publications show how the use of agent-based models, to investigate the emergency management options for floods, allows a counterfactual analysis to be easily undertaken. Counterfactual analysis is increasingly used in the field of emergency management because it enables emergency planners to show causal relationships between interventions. This allows interventions such as increasing the number of safe havens, use of new evacuation routes and improved early warnings to be compared with the existing situation, in the context of the risks that floods pose to people. The research work detailed in the nine publications has informed both policy and practice. For example, Publication 3 helped to inform the US government’s policy on increasing the resilience of vulnerable coastal in the USA to floods. The agent-based model, described in Papers 7, 8 and 9, has been used to inform emergency plans for dams in Australia, Canada, Italy, Japan, Malaysia, Peru and the USA. Overall, this series of publications points towards the need for continuous improvements in the emergency planning for low probability - high consequence flood events and the part agent-based models can play in this.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".