paper
2021
Local & station forecasting
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2021-06-05
004
Jun 2021
Fairoz Q. Kareem, Adnan Mohsin Abdulazeez, Dathar Abas Hasan
Asian Journal of Research in Computer Science
Compares neural networks, Naive Bayes, random forest and KNN for classifying weather states from a Kaggle synoptic dataset; random forest reaches 89%.
Predicting Weather Forecasting State Based on Data Mining Classification Algorithms
In one sentence. Compares neural networks, Naive Bayes, random forest and KNN for classifying weather states from a Kaggle synoptic dataset; random forest reaches 89%.
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
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.9734/ajrcos/2021/v9i330222 OpenAlex:W3169925376
- Added
- 2026-10-22
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