Flood Mitigation Approaches: Selected Cases Across Europe, Oceania and Asia
Bibliographic record
Abstract
Flood is natural phenomenon that cannot becompletelycontrolled by man. Floodis a global problem. Even urban areas whichhave good development planning and drainage systemsare susceptible to floods. Flooding in urban area is usually associated with poor drainage system maintenance, failure to plan drainage system, area development is not planned properly,and climate change factors. Typically,engineering measuresare meanttoreduce severity of flood problems and theyare quite costly. Goal of this paper is to assess the various mitigation approaches that have beenused to mitigate floodsacross Europe, Oceania and Asia continents. Selected casestudies involvingthe North Sea flood, as well as floods in the Netherlands, Scotland, New Zealand, Australia, Hong Kong, Taiwan, Singapore and Malaysiawere considered.Approaches that were highlighted include construction of dam, breakwater, canal, and pumping system. It has alsobeenfound that flood solution methods by way ofsourcecontrolis not so popular to be fully practiced. Hence, the sustainability of good development isactually a solution to problem of flooding, especially to mitigateflash floods that often occur in an urban areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".