paper
2021
Local & station forecasting
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels) Journal
2021-01-01
008
Jan 2021
Sanjaikanth E Vadakkethil Somanathan Pillai, Kiran Polimetla, Rajiv Avacharmal et al.
International Journal of Communication and Information Technology
Compares decision trees, random forests and gradient boosting for rainfall prediction from historical records, reporting better accuracy than traditional statistical methods.
Rainfall forecasting using advanced machine learning models
In one sentence. Compares decision trees, random forests and gradient boosting for rainfall prediction from historical records, reporting better accuracy than traditional statistical methods.
Labels
- Topic
- Local & station forecasting
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.33545/2707661x.2021.v2.i1a.96 OpenAlex:W4403308662
- Added
- 2026-10-22
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