paper
2018
Downscaling & super-resolution
Application study
Classical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2018-01-22
008
Jan 2018
Meysam Alizamir, Mehdi Azhdary Moghadam, Arman Hashemi Monfared et al.
Environmental Progress & Sustainable Energy
Extreme learning machine downscales reanalysis predictors to station monthly precipitation in Iran, outperforming ANN, genetic programming and quantile mapping.
Downscaling & super-resolution
Statistical downscaling of global climate model outputs to monthly precipitation via extreme learning machine: A case study
In one sentence. Extreme learning machine downscales reanalysis predictors to station monthly precipitation in Iran, outperforming ANN, genetic programming and quantile mapping.
Labels
- Topic
- Downscaling & super-resolution
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Local / station
- Data
- ERA5 / reanalysis, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1002/ep.12856 OpenAlex:W2791703741
- Added
- 2026-10-22
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