Development of a Preliminary-Risk-Based Flood Management Approach to Address the Spatiotemporal Distribution of Risk under the Kaldor–Hicks Compensation Principle
Bibliographic record
Abstract
All over the world, probability-based flood protection designs are the ones most commonly used. Different return-period design floods are standard criteria for designing structural measures. Recently, risk-based flood management has received a significant appraisal, but the fixed return period is still the de facto standard for flood management designs due to the absence of a robust framework for risk-based flood management. The objective of this paper is to discuss the economics and criteria of project appraisal, as well as to recommend the most suitable approach for a risk-based project feasibility evaluation. When it comes to flood management, decision-makers, who are generally politicians, have to prioritize the allocation of resources to different civic welfare projects. This research provides a connection between engineering, economics, and management. Taking account of socioeconomic and environmental constraints, several measures can be employed in a floodplain. The Kaldor–Hicks compensation principle provides the basis for a risk-based feasibility analysis. Floods should be managed in a way that reduces the damage from minimum investments to ensure maximum output from floodplain land use. Specifically, marginal losses due to flood damage and the expense of flood management must be minimized. This point of minimum expenses is known as the “optimum risk point” or “optimal state”. This optimal state can be estimated using a risk-based assessment. Internal rate of return, net present value, and benefit–cost ratio are indicators that describe the feasibility of a project. However, considering expected annual damage is strongly recommended for flood management to ensure a simultaneous envisage of the performance of land-use practices and flood measures. Flood management ratios can be used to describe the current ratio of expected annual damage to the expected annual damage at the optimal risk point. Further development of the approach may replace probability-based standards at the national level.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".