Policy Document · 2025-05-01
20,000 Kilometers of Code: AI's Secret Voyage Across The Ocean
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Scientific Paper
A Case Study Of An Extreme Flooding Episode In Charikar, Eastern Afghanistan
Farahnaz Fazel-Rastgar, Venkataraman Sivakumar · 2023-12-01
What it covers
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.
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
Farahnaz Fazel-Rastgar, Venkataraman Sivakumar. 2023-12-01. A Case Study Of An Extreme Flooding Episode In Charikar, Eastern Afghanistan. https://iwaponline.com/jwcc/article/14/12/4689/98655/A-case-study-of-an-extreme-flooding-episode-in (AI & Environment Resource Hub; record paper-091; 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.
lifecycle
Unclassified
orientation
Unclassified
Available source metadata
- Author or creator
- Farahnaz Fazel-Rastgar, Venkataraman Sivakumar
- Publisher
- Journal of Water and Climate Change
- Publication date
- 2023-12-01
- 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
- Water & Oceans
- Legacy subdomain
- Modeling Extreme Flooding in Charikar, Afghanistan
- journal
- Journal of Water and Climate Change
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 95 · Original ID: paper-091.
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 · 2025-04-23
A Deep-Learning Based Parameter Inversion Framework for Large-Scale Groundwater Models
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.
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.