Prediction of 3-D Ocean Temperature by Multilayer Convolutional LSTM

Sea surface temperature (SST) prediction has raised considerable attention in various ocean-related fields. However, these methods were only limited to the time-sequence prediction of some isolated points, and their spatial linkage was not considered. Furthermore, these studies only predict the temperature of sea surface, but the subsurface temperature in the inner ocean is much more important. In this letter, we propose a model of multilayer convolutional long- and short-term memory (M-convLSTM) to predict 3-D ocean temperature, comprising convolutional neural networks (CNNs), long- and short-term memory (LSTM), and multiple layer stacking to consider the horizontal and vertical temperature variations from sea surface to subsurface to be about 2000 m below. Global marine environment observation data (ARGO) are used to conduct the prediction of 3-D ocean temperature in this letter, and the results demonstrate the overall good accuracy of forecast and ARGO data.

[1]  Junyu Dong,et al.  Prediction of Sea Surface Temperature Using Long Short-Term Memory , 2017, IEEE Geoscience and Remote Sensing Letters.

[2]  M. Balmaseda,et al.  Tropical Atlantic SST Prediction with Coupled Ocean–Atmosphere GCMs , 2006 .

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[5]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[6]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[7]  Junyu Dong,et al.  A CFCC-LSTM Model for Sea Surface Temperature Prediction , 2018, IEEE Geoscience and Remote Sensing Letters.

[8]  H. U. Solanki,et al.  Integrative Analysis of AltiKa-SSHa, MODIS-SST, and OCM-Chlorophyll Signatures for Fisheries Applications , 2015 .

[9]  Qi Wang,et al.  Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[10]  R. Fablet,et al.  Colonisation tactics of three temperate catadromous species, eel Anguilla anguilla, mullet Liza ramada and flounder Plathychtys flesus, revealed by Bayesian multielemental otolith microchemistry approach , 2011 .

[11]  Arun Kumar,et al.  Prediction skill of monthly SST in the North Atlantic Ocean in NCEP Climate Forecast System version 2 , 2013, Climate Dynamics.

[12]  Nitish Srivastava,et al.  Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.

[13]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[14]  A. Hoell,et al.  The Leading Mode of Observed and CMIP5 ENSO-Residual Sea Surface Temperatures and Associated Changes in Indo-Pacific Climate , 2015 .

[15]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.