paper
2021
Local & station forecasting
Application study
RNN / LSTM / ConvLSTM6 h–3 daysNanjing University of Information Science & Technology (NUIST) Preprint
2021-04-02
014
Apr 2021
Charity Oseiwah Adjei, Wei Tian, Bernard-Marie Onzo et al.
Zenodo (CERN European Organization for Nuclear Research)
Nanjing University of Information Science & Technology (NUIST)
LSTM with correlation-based feature selection predicts hourly rainfall at Axim, Ghana, using ECMWF and station data, with MSE of 0.002.
Rainfall Forecasting in Sub-Sahara Africa-Ghana using LSTM Deep Learning Approach
In one sentence. LSTM with correlation-based feature selection predicts hourly rainfall at Axim, Ghana, using ECMWF and station data, with MSE of 0.002.
Labels
- Topic
- Local & station forecasting
- Type
- Application study
- Method
- RNN / LSTM / ConvLSTM
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.5281/zenodo.18629902 OpenAlex:W3155532891
- Added
- 2026-10-22
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