Evaluation of adaptation options to flood risk in a probabilistic framework
Bibliographic record
Abstract
City of Niamey, Niger, West Africa. This paper aims to explore the possibility of implementing a probabilistic framework for flood risk estimation for the city of Niamey, Niger. A probabilistic set of flood maps were generated by forcing a HEC-RAS model with a stochastically generated ensemble of flood peaks representing the river regime at Niamey. Loss curves were derived from expert judgment, and various adaptation options to flood risk were examined by considering two main variables: a) buildings' material; b) dike height (ranged from 180.5 m to 184 m, at a 0.5 m interval) within a scenario-based framework. Floods with return periods of 2- to 1000-yr were considered in estimating total loss, and benefits and costs of different adaptation options were compared. It was found that increasing the height of the dikes would lead to smaller economic losses, while rebuilding with better materials would increase the average annual economic losses, but might decrease the risk of human casualties. Individual and combined impact of decision variables on flood risk were estimated for the city of Niamey. Quantitative tools were developed to help decision-makers and regulators choose the best preventive measures to mitigate flood risk.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".