Datasets:

fairhealth
/

bangladesh-flood-pdna-2022

license: cc-by-4.0
task_categories:
  - tabular-regression
  - tabular-classification
language:
  - en
tags:
  - disaster-response
  - bangladesh
  - fairness
  - flood
  - humanitarian-ai
  - equity
  - pdna
size_categories:
  - n<1K

Bangladesh Flood PDNA 2022

Research datasets from the 2022 Bangladesh floods, curated by Farjana Yesmin for the paper:

Yesmin, F. & Akter, R. (2026). Toward Equitable Recovery: A Fairness-Aware AI Framework for Prioritizing Post-Flood Aid in Bangladesh. CCAI 2026 (IEEE). arXiv:2512.22210

Part of the FairHealth library — pip install fairhealth

What Is Included

File	Description	Rows
`bangladesh_floods_2022_district_level.csv`	District-level damage and loss	11
`modeling_dataset_upazila_level.csv`	ML-ready upazila features	87
`pdna_district_summary.csv`	PDNA official summary by district	11
`pdna_human_impact.csv`	Human impact indicators	—
`pdna_sector_summary.csv`	Sector-level damage breakdown	—
`fairness_metrics_summary.csv`	Fair vs baseline model comparison	—
`district_performance_comparison.csv`	MAE by district, fair vs baseline	11
`model_predictions_comparison.csv`	Model prediction outputs	87
`dataset_metadata.json`	Data provenance and field descriptions	—
`data_source_citations.txt`	Full citations for all sources	—

Key Findings

The adversarial debiasing model reduces statistical parity difference by 41.6% and regional fairness gap by 43.2% compared to baseline, with only 2.7 percentage point R² cost (0.784 vs 0.811).

Sunamganj (42.7% poverty, $159.6M damage, 94% inundation) moves from rank 14 → rank 6 under the fair model.

Data Sources

Ministry of Disaster Management and Relief, Government of Bangladesh. Post Disaster Needs Assessment: Bangladesh Floods 2022 (2023)
Bangladesh Bureau of Statistics (BBS) — poverty and population data
World Bank Bangladesh Country Data
NASA SEDAC — gridded population data
EM-DAT International Disaster Database

Usage

from fairhealth.equity.flood_aid import generate_priority_ranking
rankings = generate_priority_ranking(verbose=True)

Citation

@dataset{fairhealth_pdna_2026,
  author    = {Yesmin, Farjana and Akter, Romana},
  title     = {Bangladesh Flood PDNA 2022 Research Dataset},
  year      = {2026},
  publisher = {Hugging Face},
  doi       = {10.57967/hf/8799},
  url       = {https://huggingface.co/datasets/fairhealth/bangladesh-flood-pdna-2022}
}

Also cite:

@inproceedings{yesmin2026ccai,
  author = {Yesmin, Farjana and Akter, Romana},
  title  = {Toward Equitable Recovery: A Fairness-Aware AI Framework},
  note   = {CCAI 2026, IEEE, Nanjing. Oral. arXiv:2512.22210},
  year   = {2026}
}