paper
2022
Sub-seasonal to seasonal
Explainability, uncertainty & physics Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)RNN / LSTM / ConvLSTMCNN / U-NetSeasonal to annualNanjing University of Information Science & Technology (NUIST) Journal
2022-10-05
018
Oct 2022
Md. Jalal Uddin, Yubin Li, Md. Abdus Sattar et al.
International Journal of Climatology
Nanjing University of Information Science & Technology (NUIST)
SHAP-explained SVM, RF, CNN and LSTM forecast climatic water balance 1-3 months ahead over Bangladesh; SVM performs best with antecedent CWB inputs.
Climatic water balance forecasting with machine learning and deep learning models over Bangladesh
In one sentence. SHAP-explained SVM, RF, CNN and LSTM forecast climatic water balance 1-3 months ahead over Bangladesh; SVM performs best with antecedent CWB inputs.
Labels
- Topic
- Sub-seasonal to seasonal, Explainability, uncertainty & physics
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels), RNN / LSTM / ConvLSTM, CNN / U-Net
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- ERA5 / reanalysis, Other
- Status
- Research
- Identifiers
- DOI:10.1002/joc.7885 OpenAlex:W4301372060
- Added
- 2026-10-22
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