Flood damage assessment in a GIS – case study for annotto bay, Jamaica
Bibliographic record
Abstract
Natural hazards do not only affect millions of people, but also cause material damages up to 300 billion USD per year worldwide.The SIDS (Small Island Developing States) are characterized by an extremely high vulnerability to these hazards, due to their low-lying, densely populated cities and their fragile economy.To limit the consequences of these hazards, technocratic interventions do not suffice.Therefore, new approaches that focus on flood risk management are developed.In this context, Annotto Bay, Jamaica, was chosen as a case study area to perform a flood damage assessment.In this study, a flood damage map was created for the 2001 flood caused by Tropical Storm Michelle.This map focuses on three types of damage: building, road and crop damage.The first type was calculated using the exact GPS locations of the buildings, as well as average replacement values for each building type and flood damage functions.The total building damage was then combined per land-use polygon to have an orderly visual view of the damage spread.Furthermore, the road damage was calculated, based on a road network extracted from satellite imagery.In a next step, as buildings are mostly located in proximity of roads, buffers were created around the road network, resulting in a more accurate visual view of the building damage spread.Then the crop damage was calculated based on maximum damage values for banana plantains and other crops, combined with the crop damage functions.The final result is a total damage map, visualizing the location of high risk areas with a high accuracy.Additionally, the total calculated damage was compared to the actual damage caused by the 2001 flood.This comparison shows promising results.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".