paper
2021
Observations & remote sensing
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels) Other
2021-01-01
012
Jan 2021
Andrey Konstantinovich Gorshenin, Svetlana Lukina
Advances in intelligent systems and computing
Compares random forests, XGBoost, SVM and EM for imputing up to 40% missing precipitation at over a hundred stations; XGBoost combinations reach nRMSE 0.01–0.07.
On the efficiency of machine learning algorithms for imputation in spatiotemporal meteorological data
In one sentence. Compares random forests, XGBoost, SVM and EM for imputing up to 40% missing precipitation at over a hundred stations; XGBoost combinations reach nRMSE 0.01–0.07.
Labels
- Topic
- Observations & remote sensing
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Global
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- OpenAlex:W3175667698
- Added
- 2026-10-22
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