paper
2019
Local & station forecasting
New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2019-06-27
004
Jun 2019
Atiek Iriany, Diana Rosyida, Arifin Arifin
Communications in Statistics - Simulation and Computation
Hybrid GSTAR-SUR with neural network residual model improves 10-day precipitation forecasts at four West Java stations, reducing mean error from 4.33 to 3.89 mm.
A comparison of GSTAR-SUR models and a hybrid GSTAR-SUR/neural network model on residuals of precipitation forecasting
In one sentence. Hybrid GSTAR-SUR with neural network residual model improves 10-day precipitation forecasts at four West Java stations, reducing mean error from 4.33 to 3.89 mm.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1080/03610918.2019.1615625 OpenAlex:W2954957447
- Added
- 2026-10-22
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