paper
2019
Local & station forecasting
Extremes & hazards New model or method
RNN / LSTM / ConvLSTM6 h–3 days Journal
2019-10-24
008
Oct 2019
Chih‐Chiang Wei, Ju-Yueh Cheng
Journal of Hydroinformatics
Deep recurrent networks in a two-step model predict typhoon wind speed and wave height at a Taiwan buoy, beating a one-step wave-height baseline.
Nearshore two-step typhoon wind-wave prediction using deep recurrent neural networks
In one sentence. Deep recurrent networks in a two-step model predict typhoon wind speed and wave height at a Taiwan buoy, beating a one-step wave-height baseline.
Labels
- Topic
- Local & station forecasting, Extremes & hazards
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.2166/hydro.2019.084 OpenAlex:W2981339097
- Added
- 2026-10-22
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