A comprehensive assessment of multilayered safety (meerlaagsveiligheid) in flood risk management
Bibliographic record
Abstract
Multilayered Safety (MLS) is seen as the next step in Dutch flood risk management. In the last decades the idea that only flood defenses can prevent floods gave way to the realization prevention can also be implemented along other lines, e.g. giving the rivers more space. The next thought was that next to preventing floods it should be possible to reduce the loss due to flooding. Therefore, MLS is meant to introduce comprehensive flood risk management by implementing three layers, or put differently safety nets: 1. Prevention (dikes, space for rivers, etc.), 2. Spatial Solutions (flood-proofing houses, elevating houses, re-locating etc.), 3. Crisis Management (evacuation, warning, etc.). Before this study, there was no academic interpretation of MLS and it had never been tested comprehensively. Consequently, a theoretical framework is being developed in this thesis to be able to model MLS. This is followed by a hypothetical case study and additional one for the City of Dordrecht to examine the actual effect of MLS on the flood risk and its cost-efficiency. It was found that theoretically MLS is indeed an alternative to only Prevention. Furthermore, it introduces the option to better customize flood risk management to local circumstances. By doing so, flood risk management becomes more cost-efficient. As the cost-efficiency is found to be dependent on the initial safety level, it is concluded that in the Netherlands MLS only has the potential to supplement the existing flood protection. In areas with a heavy implementation of flood defenses like in Dordrecht, MLS is fit to complement flood risk management rather than replacing the prevailing Prevention approach. However, to do so (local) authorities need to be able to base their flood management policies on flood risk, e.g. by benchmarking a certain Individual 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".