Scientific Paper · 2025-01-19
3D Cloud Reconstruction Through Geospatially-Aware Masked Autoencoders
Self-supervised learning enhances 3D cloud reconstruction, improving climate predictions by reducing uncertainties in climate models.
Scientific Paper · source-verified
Assessing the Impact of Climate Change on Flood Patterns in Downstream Nigeria Using Machine Learning and Geospatial Techniques (2018–2024)
Eteh, Desmond R.; Japheth, Bunakiye R.; Akajiaku, Charles U.; Osondu, Ifunanya; Mene-Ejegi, Omabuwa O.; Nwachukwu, Emmanuel M.; Oriasi, Miebaka D.; Omietimi, Erepamo J.; Ayo-Bali, Abiodun E. · 2025-07-02
This study uses machine learning and geospatial analysis to assess how climate change has altered flood patterns in downstream Nigeria between 2018 and 2024, revealing shifts in flood frequency, intensity, and spatial distribution.
Show citation
Eteh, Desmond R.; Japheth, Bunakiye R.; Akajiaku, Charles U.; Osondu, Ifunanya; Mene-Ejegi, Omabuwa O.; Nwachukwu, Emmanuel M.; Oriasi, Miebaka D.; Omietimi, Erepamo J.; Ayo-Bali, Abiodun E.. 2025-07-02. Assessing the Impact of Climate Change on Flood Patterns in Downstream Nigeria Using Machine Learning and Geospatial Techniques (2018–2024). https://link.springer.com/article/10.1007/s44288-025-00178-7 (AI & Environment Resource Hub; record paper-328; 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
Unclassified
orientation
Available source metadata
- Author or creator
- Eteh, Desmond R.; Japheth, Bunakiye R.; Akajiaku, Charles U.; Osondu, Ifunanya; Mene-Ejegi, Omabuwa O.; Nwachukwu, Emmanuel M.; Oriasi, Miebaka D.; Omietimi, Erepamo J.; Ayo-Bali, Abiodun E.
- Publisher
- Discover Geoscience
- Publication date
- 2025-07-02
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-06T18:16:29.762Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Climate & Weather
- Legacy subdomain
- Disaster Risk & Climate Resilience in Downstream Nigeria
- journal
- Discover Geoscience
Provenance and review
Source sheet: Scientific Paper · Row 332 · Original ID: paper-328.
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.
Suggest a correction for this record →Related through shared environmental topics
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Research tools and geospatial methods; Climate and greenhouse gases; Weather, hazards, and adaptation
Shared topics: Research tools and geospatial methods; 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.
Shared topics: Research tools and geospatial methods; Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-06-01
Advancing Hourly Gross Primary Productivity Mapping Over East Asia Using LGBM
This study presents a new machine learning (Light Gradient Boosting Machine) approach to generate high-resolution, hourly maps of gross primary productivity (GPP) for East Asia (2020–2021). The paper highlights clear diurnal patterns across different land cover types and latitudes, offering valuable insights for ecosystem monitoring and carbon cycle modeling.
Shared topics: Research tools and geospatial methods; Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-01-14
Assessing the Size of Spatial Extreme Events Using Local Coefficients Based on Excursion Sets
A new family of spatial-extent coefficients assesses extreme georeferenced events by measuring spatial spread from threshold exceedances, with a semiparametric model enabling statistical extrapolation, demonstrated through simulations and gridded temperature data in France.