paper
2023
Local & station forecasting
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2023-09-25
004
Sep 2023
Deep Karan Singh, Nisha Rawat
International Journal of Intelligent Systems and Applications
Compares XGBoost, SVM and Random Forest for daily maximum and minimum temperature at Visakhapatnam; no single model dominates.
Machine Learning for Weather Forecasting: XGBoost vs SVM vs Random Forest in Predicting Temperature for Visakhapatnam
In one sentence. Compares XGBoost, SVM and Random Forest for daily maximum and minimum temperature at Visakhapatnam; no single model dominates.
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:10.5815/ijisa.2023.05.05 OpenAlex:W4387029677
- Added
- 2026-10-22
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