paper
2022
Sub-seasonal to seasonal
Post-processing & calibration New model or method
RNN / LSTM / ConvLSTMCNN / U-NetSeasonal to annual Journal
2022-01-01
012
Jan 2022
Jahnavi Jonnalagadda, Mahdi Hashemi
International Journal of Software Engineering and Knowledge Engineering
A convolutional LSTM with backward stepwise feature selection forecasts the Oceanic Niño Index 1 to 12 months ahead, with SST the best predictor.
Feature Selection and Spatial-Temporal Forecast of Oceanic Niño Index Using Deep Learning
In one sentence. A convolutional LSTM with backward stepwise feature selection forecasts the Oceanic Niño Index 1 to 12 months ahead, with SST the best predictor.
Labels
- Topic
- Sub-seasonal to seasonal, Post-processing & calibration
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, CNN / U-Net
- Time scale
- Seasonal to annual
- Domain
- Global
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1142/s0218194022500048 OpenAlex:W4213445549
- Added
- 2026-10-22
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