基于LSTM网络的海水温度剖面预报研究OA北大核心CSTPCD

Based on ocean temperature historical observations and ocean model data,the short-term forecasting method for ocean temperature profile is studied using Long Short-Term Memory(LSTM)neural network.For the ocean temperature profile forecasts at(17°46.91'N,112°03.24'E)in the South China Sea,three sample sets of observation,forecasts,mixed observation-forecasts are constructed using the observational and forecasting data.Based on the LSTM neural network,a many-to-many ocean temperature profile forecasting model composed of encoder and decoder is established,and model training and verification are carried out.The results show that the model has a high forecasting accuracy and a good stability for processing small sample problems.Utilizing deep layered forecasting method can effectively improve the forecasting accuracy and generalization ability.The forecasting error of mixed sample set decreases significantly in comparison with that of observation sample set,which provides an idea for marine environment forecasts of small sample problems.