Are there better ways to quantify flood risk to life
Bibliographic record
Abstract
Risk to life is a critical consideration in flood risk management but quantify those risks have long been a vexed issue. The Australian National Committee on Large Dams (ANCOLD, 2003) recommends the methodology developed by Graham (1999) for estimating loss of life from dam failure. In the absence of other accepted methodologies this has sometimes been applied to loss of life from flooding generally but Graham himself has stressed on more than one occasion that the method has been developed purely for dam failure scenarios and is not suitable for other flood events (Graham, 2013). \n \nIn recent years more sophisticated models for the estimation of loss of life in any flood event have been created. One of the most advanced of these was developed by BC Hydro in Canada and recently commercialised as the Life Safety Model by HR Wallingford in the UK. It is an agent based model which integrates dynamic 2D flood modelling with a flood warning dissemination model, a dynamic traffic model and consequence analysis of the interaction of floodwaters with people, vehicles and buildings to track the warning, response, evacuation and fate of each individual on a floodplain. \n \nMolino Stewart and HR Wallingford were engaged by the NSW State Emergency Service to pilot the use of this model at Windsor on the Hawkesbury Nepean Floodplain and evaluate its utility for both evacuation planning and life risk quantification. This paper presents the findings of that work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.012 | 0.036 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".