paper
2023
Benchmarks, datasets & evaluation
Extremes & hazardsExplainability, uncertainty & physics Evaluation or intercomparison of existing models
CNN / U-Net Journal
2023-09-26
012
Sep 2023
Noelia Otero, Pascal Horton
Water Resources Research
Compares deep learning architectures for European precipitation, including extremes; a two-level U-Net matches deeper versions at lower cost, with layer-wise relevance attribution.
Benchmarks, datasets & evaluation
Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes
In one sentence. Compares deep learning architectures for European precipitation, including extremes; a two-level U-Net matches deeper versions at lower cost, with layer-wise relevance attribution.
Labels
- Topic
- Benchmarks, datasets & evaluation, Extremes & hazards, Explainability, uncertainty & physics
- Type
- Evaluation or intercomparison of existing models
- Method
- CNN / U-Net
- Domain
- Regional
- Status
- Research
- Identifiers
- DOI:10.1029/2023wr035088 OpenAlex:W4387055315
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.