Policy Document · 2025-05-01
20,000 Kilometers of Code: AI's Secret Voyage Across The Ocean
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Scientific Paper
A Deep-Learning Based Parameter Inversion Framework for Large-Scale Groundwater Models
Triplett et al. · 2025-04-23
What it covers
A machine learning framework was developed to efficiently parametrize large-scale hydrogeologic models by learning the relationship between water table depth and hydraulic conductivity from a small number of simulations, enabling realistic predictions with significantly reduced computational cost.
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
Triplett et al.. 2025-04-23. A Deep-Learning Based Parameter Inversion Framework for Large-Scale Groundwater Models. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL114285 (AI & Environment Resource Hub; record paper-256; 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
- Triplett et al.
- Publisher
- AGU
- Publication date
- 2025-04-23
- 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
- Water & Oceans
- Legacy subdomain
- Hydroinformatics and Machine Learning for Groundwater Modeling
- journal
- AGU
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 260 · Original ID: paper-256.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source blocks automated access · 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: Water; Oceans and coasts
Shared topics: Water; Oceans and coasts
Scientific Paper · 2023-12-01
A Case Study Of An Extreme Flooding Episode In Charikar, Eastern Afghanistan
Severe flash floods in Charikar, Afghanistan, on August 26, 2020, were driven by extreme atmospheric instability, deep low-level convergence, and local topography, with climate change intensifying frontal activity and baroclinicity in the region.
Shared topics: Water; Oceans and coasts
Scientific Paper · 2025-09-25
A Sentinel-3 Foundation Model for Ocean Colour
This study introduces a new foundation model based on the Prithvi-EO Vision Transformer, pre-trained on Sentinel-3 ocean color data, to enhance marine Earth observation. By fine-tuning on chlorophyll and primary production tasks, the model outperforms traditional baselines, showing that self-trained AI can extract detailed spatial patterns from limited labeled data and improve monitoring of ocean ecosystems and climate processes.
Shared topics: Water; Oceans and coasts
Organization · Africa
ABALOBI
ABALOBI – meaning fisher in the isiXhosa language – is a South African-based organisation with global reach. Through our world-first fisheries improvement technologies and programmes, we strive to poise small-scale fisheries for ecological, economic and social sustainability.