Policy Document · 2024-12-01
2024 Climate Risk Landscape Report
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Scientific Paper
Comparative Analysis of Artificial Intelligence Models for Real-Time and Future Forecasting of Environmental Conditions: A Wood-Frame Historic Building Case Study
Faubel et al. · 2024-12-01
What it covers
Machine learning enhances real-time and future climate monitoring in historic buildings, aiding preservation efforts amid climate change.
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
Faubel et al.. 2024-12-01. Comparative Analysis of Artificial Intelligence Models for Real-Time and Future Forecasting of Environmental Conditions: A Wood-Frame Historic Building Case Study. https://www.sciencedirect.com/science/article/pii/S2352710224030420?via%3Dihub (AI & Environment Resource Hub; record paper-009; 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
- Faubel et al.
- Publisher
- Journal of Building Engineering
- Publication date
- 2024-12-01
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source access
- Automated access restricted
- Metadata confidence
- Metadata unconfirmed
- Last verified
- Not yet verified
- Snapshot import
- 2026-09-06
- Legacy domain
- Climate & Weather
- Legacy subdomain
- AI for Environmental Forecasting
- journal
- Journal of Building Engineering
Provenance and review
- Source access
- Automated access restricted
- 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 13 · Original ID: paper-009.
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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Related through shared environmental topics
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Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
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Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-05-21
A Foundation Model for the Earth System
Aurora is a large-scale AI foundation model that significantly outperforms traditional forecasting systems across multiple Earth system domains, like air quality and cyclone tracking, while using far less computational power, marking a major advancement in accessible, efficient environmental prediction.
Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-04-23
A Hackathon for Flood Map Prediction From Geospatial Data With Parsimonious Machine Learning Models
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.