Application of convolutional neural network to sea surface temperature prediction in the coastal waters
Concerning the low sea surface temperature(SST)prediction accuracy of numerical modeling and empirical methods in near-shore stations,we consider sea surface temperature prediction as forecasting of multivariate time series data,construct the sea surface temperature time series model of near-shore stations by convolutional neural network(CNN)to predict the maximum,minimum and mean sea surface temperature for the next day,and compare CNN model with empirical forecast method and long short-term memory(LSTM)model through experiment.The experimental results show that compared with empirical forecast method,the mean absolute error(MSE)of CNN model on daily maximum SST forecast drops 0.14℃ to 0.36℃,root mean squared error(RMSE)drops 0.21℃ to 0.49℃,the MSE of CNN model on daily minimum SST forecast drops 0.17℃ to 0.36℃,RMSE drops 0.24℃ to 0.63℃,the MSE of CNN model on daily mean SST forecast is 0.30℃,RMSE is 0.47℃,its forecast performance is as good as LSTM model in the testing set.It shows that the application of CNN to SST modeling is feasible,improve the accuracy of sea surface temperature prediction which can compare favorably with LSTM model.
sea surface temperaturenear-shore stationsmultivariate time seriesCNN
万方数据
维普
