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Scientific Paper · source-verified
A Hackathon for Flood Map Prediction From Geospatial Data With Parsimonious Machine Learning Models
Medernach et al. · 2025-04-23
This study showcases a machine learning hackathon in France where a CNN-based model accurately predicted flood risk evolution using geospatial and climate data, even without streamflow input, emphasizing trustworthy AI in data-scarce regions.
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Medernach et al.. 2025-04-23. A Hackathon for Flood Map Prediction From Geospatial Data With Parsimonious Machine Learning Models. https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/iclr2025/5/paper.pdf (AI & Environment Resource Hub; record paper-250; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
topic
lifecycle
orientation
Available source metadata
- Author or creator
- Medernach et al.
- Publisher
- ICLR
- Publication date
- 2025-04-23
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-15T16:44:41.114Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Climate & Weather
- Legacy subdomain
- Flood Risk Prediction in Data-Scarce Environments
- journal
- ICLR
Provenance and review
Source sheet: Scientific Paper · Row 254 · Original ID: paper-250.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
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