Scientific Paper

A Hackathon for Flood Map Prediction From Geospatial Data With Parsimonious Machine Learning Models

Medernach et al. · 2025-04-23

What it covers

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.

Who it helps

Intended audience has not been documented.

Limitations

Source-specific limitations have not been reviewed. Inclusion and topic labels do not establish effectiveness or reliability.

Show citation

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 2026-10-04).

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.

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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 access
Reachable at last check
Metadata confidence
Metadata unconfirmed
Last verified
Not yet verified
Snapshot import
2026-09-06
Legacy domain
Climate & Weather
Legacy subdomain
Flood Risk Prediction in Data-Scarce Environments
journal
ICLR

Provenance and review

Source access
Reachable at last check
Metadata confidence
Metadata unconfirmed
Editorial relevance
Relevance has not been separately reviewed.
Evidence quality
Evidence quality has not been separately assessed.

Source sheet: Scientific Paper · Row 254 · Original ID: paper-250.

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A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-28. Metadata not yet corroborated. The import date is not the original date added.

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