paper
2019
Sub-seasonal to seasonal
Local & station forecasting New model or method
Classical ML (trees, GP, linear, kernels)2 weeks–2 months Journal
2019-09-28
012
Sep 2019
Laleh Parviz, Kabir Rasouli
Journal of Hydrologic Engineering
Hybrid clustering, ARIMA and neural network model forecasts subseasonal station precipitation, reducing RMSE by 24–48% versus seasonal ARIMA in Iran.
Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering
In one sentence. Hybrid clustering, ARIMA and neural network model forecasts subseasonal station precipitation, reducing RMSE by 24–48% versus seasonal ARIMA in Iran.
Labels
- Topic
- Sub-seasonal to seasonal, Local & station forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 2 weeks–2 months
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1061/(asce)he.1943-5584.0001862 OpenAlex:W2976057303
- Added
- 2026-10-22
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