paper
2022
Local & station forecasting
New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2022-08-30
008
Aug 2022
Vimal Raja Gopinathan
International Journal of Multidisciplinary Research in Science, Engineering and Technology.
Random Forest and SVM predict 12-hour snowfall occurrence from meteorological and terrain data, with RF RMSE of 3.2 cm versus 5.1 cm for regression.
Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration
In one sentence. Random Forest and SVM predict 12-hour snowfall occurrence from meteorological and terrain data, with RF RMSE of 3.2 cm versus 5.1 cm for regression.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ, Other
- Status
- Research
- Identifiers
- DOI:10.15680/ijmrset.2022.0508010 OpenAlex:W4408467528
- Added
- 2026-10-22
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