paper
2022
Local & station forecasting
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2022-01-01
004
Jan 2022
Iliyas Ibrahim Iliyas, Andra Umoru, Adati Elkanah Chahari et al.
Journal of Artificial Intelligence and Systems
Compares ridge, random forest, linear regression and decision tree for temperature prediction; the decision tree gets the lowest RMSE of 0.036.
Performance Evaluation of Machine Learning Models for Weather Forecasting
In one sentence. Compares ridge, random forest, linear regression and decision tree for temperature prediction; the decision tree gets the lowest RMSE of 0.036.
Labels
- Topic
- Local & station forecasting
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.33969/ais.2022040102 OpenAlex:W4312509337
- Added
- 2026-10-22
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