paper
2022
Extremes & hazards
Nowcasting New model or method
Classical ML (trees, GP, linear, kernels)CNN / U-NetRNN / LSTM / ConvLSTM0–6 hNanjing University of Information Science & Technology (NUIST) Journal
2022-07-05
022
Jul 2022
Md. Jalal Uddin, Yubin Li, Md. Abdus Sattar et al.
Journal of Geophysical Research Atmospheres
Nanjing University of Information Science & Technology (NUIST)
Cluster-wise typhoon rainfall model combines SVM, RF, AdaBoost, CNN and LSTM with grid-search tuning, claiming 45–90% forecast efficiency gains for 1–6 h lead times.
An Improved Cluster‐Wise Typhoon Rainfall Forecasting Model Based on Machine Learning and Deep Learning Models Over the Northwestern Pacific Ocean
In one sentence. Cluster-wise typhoon rainfall model combines SVM, RF, AdaBoost, CNN and LSTM with grid-search tuning, claiming 45–90% forecast efficiency gains for 1–6 h lead times.
Labels
- Topic
- Extremes & hazards, Nowcasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels), CNN / U-Net, RNN / LSTM / ConvLSTM
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Station / in-situ, Other
- Status
- Research
- Identifiers
- DOI:10.1029/2022jd036603 OpenAlex:W4283815051
- Added
- 2026-10-22
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