# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 5 Conclusions  
In this paper, we analyzed the temporal dependence (closeness, period, and trend) of marine temperature, and proposed a method based on temporal dependence parameter matrix fusion for historical observation ocean temperature data. Using the fusion data, the LSTM network, a time-dependent LSTM ocean temperature prediction model is trained and the method is proposed. We evaluated our model using BOA_Argo data. Experiments have shown that the TD-LSTM and its variants are better than SVR and MLPR in multiple seas (the Coral Sea, the Equatorial Pacific Region, and the South China Sea). The input sequence length that we used to train the model can be reduced by using the TD-LSTM method. In terms of prediction accuracy, the performance of TD-LSTM is close to or even better than those methods, whose time span are similar to TD-LSTM, but the input sequence length is much longer than it. The superior performance of the TD-LSTM was demonstrated at multiple depths in multiple sea areas.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] improved the design of experiment; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] wrote the paper.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61631008), the Fundamental Research Funds for the Central Universities 2017 TD-18, and the State Key Laboratory of Robotics No. 2015-O06. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. ([[http://www.argo.ucsd.edu](http://www.argo.ucsd.edu)]([http://www.argo.ucsd.edu](http://www.argo.ucsd.edu)), [[http://argo.jcommops.org](http://argo.jcommops.org)]([http://argo.jcommops.org](http://argo.jcommops.org))). The Argo Program is part of the Global Ocean Observing System. Here, we also want to thank the National Natural Science Foundation of China under Grants No. 61572172 and No. 61872124, the National Natural Science Foundation of China-Guangdong Joint Fund under Grant No. U180120020, the Fundamental Research Funds for the Central Universities, No. DUT17 RC(3)094, program for Liaoning Excellent Talents in University, No. LR2017009.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Region** & \multicolumn{3}{c}{**Coral Sea**} & \multicolumn{3}{c}{**Equatorial Pacific Region**} & \multicolumn{3}{c}{**South China Sea**} \\ \hline
**Methods** & **0 m** & **200 m** & **500 m** & **0 m** & **200 m** & **500 m** & **0 m** & **200 m** & **500 m** \\ \hline
8-LSTM & 1.7\% & 17\% & 5.7\% & 13.2\% & 14.2\% & \(-\)6.0\% & 16.0\% & 13.9\% & 8.8\% \\
24-LSTM & 4.6\% & \(-\)0.4\% & 7.9\% & 15.5\% & 8.3\% & 0.03\% & 10.3\% & 11.9\% & 10.1\% \\ TD-SVR & 4\% & 51.7\% & 50.8\% & 32.4\% & 62.1\% & 46.5\% & 7.2\% & 36.8\% & 36.4\% \\
8-SVR & 25.2\% & 31.5\% & 41.9\% & 26.4\% & 71.6\% & 35.8\% & 20.3\% & 31.8\% & 35.2\% \\
24-SVR & 5.9\% & 51.8\% & 48.1\% & 35.4\% & 66.7\% & 52.6\% & 3.9\% & 33.7\% & 37.1\% \\ TD-MLPR & 11.9\% & 57.1\% & 54.6\% & 40.3\% & 62.5\% & 56.1\% & 31.4\% & 45.4\% & 43.4\% \\
8-MLPR & 40.9\% & 38.3\% & 53.6\% & 41.4\% & 71.3\% & 42.1\% & 53.7\% & 34.5\% & 41.2\% \\
24-MLPR & 36.2\% & 55.4\% & 60\% & 45.2\% & 70.0\% & 64.6\% & 32.9\% & 54.5\% & 58.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of TD-LSTM performance improvement rates.  
Figure 10: Comparison of performances at different depth in the South China Sea.