paper
2020
Local & station forecasting
New model or method
Classical ML (trees, GP, linear, kernels)Institute of Atmospheric Physics, Chinese Academy of Sciences Journal
2020-08-26
018
Aug 2020
Chen Yu, Haochen Li, Jiangjiang Xia et al.
Communications in Computational Physics
Institute of Atmospheric Physics, Chinese Academy of Sciences
Random Subfeature Ensemble Learning (RSEL) combines random feature subsets and ensembling to improve wind speed and direction forecasts over traditional methods, with claimed robustness.
A Data-Driven Random Subfeature Ensemble Learning Algorithm for Weather Forecasting
In one sentence. Random Subfeature Ensemble Learning (RSEL) combines random feature subsets and ensembling to improve wind speed and direction forecasts over traditional methods, with claimed robustness.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.4208/cicp.oa-2020-0006 OpenAlex:W3082642777
- Added
- 2026-10-22
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