Integrated assessment of flood risk in Arial Khan floodplain of Bangladesh under changing climate and socioeconomic conditions
Bibliographic record
Abstract
Abstract In the assessment of flood risk, the future flood hazard due to climate change is often tied to the present socioeconomic conditions. This makes an implicit assumption that the drivers of risk, other than the hazard, remain constant with time. Therefore, such risk assessment does not provide a realistic outlook for devising plausible mitigation strategies and plans. In this study, flood risk was assessed from an integrated perspective by considering both physical hazard, and socioeconomic exposure and vulnerability—all changing with time. The flood hazard in the Arial Khan River floodplain in the southcentral Bangladesh was simulated with a two‐dimensional hydrodynamic model, and the exposure and vulnerability were projected using different statistical techniques. Principal component analysis was conducted to assign weights to the indicators of hazard, exposure, sensitivity, and adaptive capacity. The results show that the flood depth, duration, and extent would increase from the baseline to 2080s under regional concentration pathway (RCP) 2.6 and RCP 8.5 scenarios. The sensitivity and vulnerability would decrease, reflecting an improved adaptive capacity. The low‐risk areas could increase from 62% in the baseline to 85%–91% in 2080s depending on the RCPs. The approach followed can be applied elsewhere in developing countries, particularly in riverine floodplain settings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".