Using Meteorological and Geospatial Data to Investigate November 2013 Typhoon Haiyan’s Impact in the Philippines
Bibliographic record
Abstract
Typhoons (also known as hurricanes or tropical cyclones) can cause widespread destruction, particularly in vulnerable countries like the Philippines. Historical impact data provide valuable insights into factors influencing typhoon damage and can help improve disaster response by identifying heavily and mildly affected areas. This study utilizes Google Earth Engine to access and analyze meteorological and geospatial data in early November 2013, at the municipal level for Typhoon Haiyan, one of the most destructive typhoons in recent history. Hazard impact data from the National Disaster Risk Reduction and Management Council (NDRRMC) were analyzed using correlation, multiple linear regression, and clustering methods. Results indicate that windspeed and rainfall are dominant factors in determining the percentage of persons affected. Clustering analysis reveals that municipalities experiencing windspeeds exceeding 11.51 m/s suffered the greatest impacts. These findings highlight important factors influencing variability in typhoon impacts. This approach offers a useful framework for rapid impact assessment of future typhoons in the Philippines and elsewhere.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".