Geospatial Approach to Assess Flash Flood Vulnerability in a Coastal District of Bangladesh: Integrating the Multifaceted Dimension of Vulnerabilities
Bibliographic record
Abstract
Flash floods pose a significant threat to Bangladesh; in particular, on 20 August 2024, the Feni district experienced a major flash flood, affecting more than 550,000 people and causing widespread damage. To effectively mitigate the impacts of flash floods, it is essential to conduct a comprehensive flash flood vulnerability assessment, incorporating multiple triggering factors. This study aims to assess flash flood vulnerability in the Feni District through a unique approach, integrating various dimensions of vulnerability. The study utilizes a geospatial methodology, employing the formula of vulnerability developed by UNESCO-IHE. Four dimensions of vulnerability were analyzed: social, physical, economic, and environmental. For each dimension, specific variables were selected to assess exposure, susceptibility, and resilience. Principal Component Analysis (PCA) was used to assign weights to these variables. The geospatial layers of influencing vulnerability factors were integrated together to create flash flood vulnerability maps of four dimensions. These were then overlaid to generate a composite flash flood vulnerability map. The analysis revealed a distinct spatial distribution of vulnerability across Feni District. In terms of environmental vulnerability due to flash flood, about 14% of the total area falls into the very highly vulnerable zone, whereas 13%, 8% and 5% of the study area were found to be very highly vulnerable regarding social, economic and physical aspects, respectively. The composite flash flood vulnerability map identified key vulnerability hotspots, with the most vulnerable unions (the smallest administrative unit in Bangladesh) being Feni Pourashava (68% very high), Sonagazi Paurashava (40% very high), and Nawabpur (32% very high), while the least vulnerable areas were Jailashkara (58% very low), Anandapur (81% very low), and Darbarpur (82% very low). The results show that the Feni District’s flash flood susceptibility varies significantly throughout the region, which provide crucial insights for policymakers and local authorities in order to identify vulnerability hotspots, prioritize interventions in vulnerable areas, enhance flash flood resilience, and implement adaptive strategies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".