Multi-dimensional flood vulnerability assessment using data envelopment analysis
Bibliographic record
Abstract
Malaysia has been greatly impacted by flood during monsoon seasons. Even though flood prone areas are well identified, assessment on the vulnerability of the disaster is lacking. Assessment of flood vulnerability, defined as the potential for loss when a disaster occurs, is addressed in this paper. The focus is on the development of flood vulnerability measurement in 11 states in Peninsular Malaysia using a non-parametric approach of Data Envelopment Analysis. Scores for three dimensions of flood vulnerability (Population Vulnerability, Social Vulnerability and Biophysical) were calculated using secondary data of selected input and output variables across an 11-year period from 2004 to 2014. The results showed that Johor and Pahang were the most vulnerable to flood in terms of Population Vulnerability, followed by Kelantan, the most vulnerable to flood in terms of Social Vulnerability and Kedah, Pahang and Terengganu were the most vulnerable to flood in terms of Biophysical Vulnerability among the eleven states. The results also showed that the state of Johor, Pahang and Kelantan to be most vulnerable across the three dimensions. Flood vulnerability assessment is important as it provides invaluable information that will allow the authority to identify and develop plans for flood mitigation and to reduce the vulnerability of flood at the affected regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 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 teacher head, 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".