Assessing damage cost estimation of urban pluvial flood risk as a mean of improving climate change adaptations investments
Bibliographic record
Abstract
Estimating the expected annual damage (EAD) due to flooding in an urban area is of great interest for urban water managers and other stakeholders. It is a strong indicator for a given area showing how it will be affected by climate change and how much can be gained by implementing adaptation measures. This study investigates three different methods for estimating the EAD based on a loglinear relation between the damage costs and the return periods, one of which has been used in previous studies. The results show with the increased amount of data points there appears to be a shift in the log-linear relation which could be contributed by the Danish design standards for drainage systems. Three different methods for estimating the EAD were tested and the choice of method is less important than accounting for the log-linear shift. This then also means that the statistical approximation of the EAD used in previous studies appears to be valid and is a good assumption. The EAD estimation can be simplified by having a single unit cost per flooded area which is multiplied with the extent of the flood. It does however depend on the lower threshold chosen in the estimation of the flood extent.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".