Assessment of flood susceptibility in Sylhet using analytical hierarchy process and geospatial technique
Bibliographic record
Abstract
In Bangladesh, flooding has been one of the most devastating natural disasters, and the susceptibility assessment of flood is a major precondition for minimizing the impact and making a sustainable future. Therefore, identifying and assessing susceptibility is essential to reduce the number of casualties that result from flooding events in the future. The primary challenge in flood risk assessment is developing a systematic understanding of all potential damages associated with a flood event. Keeping this goal in mind, the present study focuses on estimating the flood susceptibility in Sylhet district of Bangladesh. This study employs the combination of Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) for Flood Susceptibility Assessment (FSA) of Sylhet district in Bangladesh. The study examines the significance of every element involved in FSA and provides explicit descriptions for each of them. Applying multiple-criteria computation techniques, the analysis focuses on computing inundation-related calculations and generating map depicting flood susceptible zones. Concerning flooding event, the district Sylhet exhibits varying degrees of flood susceptibility, with 14.71 % of the area categorized as having very high susceptibility, 18.70 % as high susceptibility, 25.17 % as medium susceptibility, 24.94 % as low susceptibility, and 16.47 % of the study areas identified with very low susceptibility. The present research on flood susceptibility assessment could help to minimize the losses and lower the risk associated with flooding. • Flood susceptibility of Sylhet district of Bangladesh has been estimated. • Analytical hierarchy process and geospatial techniques have been applied. • Factors that influence flood susceptibility are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".