Prediction of sea surface temperature based on ConvGRU deep learning network model
ZHANG Xuewei,HAN Zhen*
1.College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China;2.Shanghai Engineering Research Center of Estuarine and Oceanographic Mapping,Shanghai 201306,China
Abstract: In order to solve the problems of insufficient combination of temporality and spatiality of traditional time series network models and insufficient ability of batch processing large data of marine environmental elements,sea surface temperature (SST) prediction and analysis were carried out using the SST grid data from 1999 to 2019 in part of the Northwest Pacific Ocean (OISST Products) by ConvGRU deep learning neural network model,that is,combination of the extended algorithm of recurrent neural network (RNN) and convolutional neural network (CNN),and by sample generator to process data,which can effectively deal with the batch problem of time series remote sensing data.The results showed that the ConvGRU model training set had root mean square error of 0.044 9 ℃ and accuracy of 99.69%,and that the validation set had root mean square error of 0.045 2 ℃ and accuracy of 99.64%.Finally,the model established in this paper was used to predict the sea surface temperature data in 2020,with the root mean square error of 0.047 8 ℃ and accuracy of 99.60% in the test set.The average mean absolute deviation of SST prediction was shown to be about 0.379 3 ℃,and the average prediction accuracy to be 97.31%.The findings indicate that the ConvGRU neural network model can predict the trend of sea surface temperature well,which provides a feasible method for SST neural network prediction model.
张雪薇, 韩震. 基于ConvGRU深度学习网络模型的海表面温度预测[J]. 大连海洋大学学报, 2022, 37(3): 531-.
ZHANG Xuewei, HAN Zhen. Prediction of sea surface temperature based on ConvGRU deep learning network model. Journal of Dalian Ocean University, 2022, 37(3): 531-.
2022, Vol. 37 
