Research on multivariate Yellow Sea SST week prediction method based on encoder-decoder LSTM
Journal: Monitoring systems of environment
Year: 2022

In order to further improve the accuracy and stability of sea surface temperature forecasting, this paper uses the 25-year historical data of OISST V2.0 and OAFlux, and fully considers factors such as radiation flux, heat flux, wind speed, air temperature, air specific humidity and SST. By controlling the variables and selecting the best model parameters, a multivariate Yellow Sea SST weekly prediction model based on the Encoder-Decoder LSTM (Long Short Term Memory) was constructed for the first