Prediction of sea temperature using temporal convolutional network and LSTM-GRU network

Abstract

The ocean is a complex system. Ocean temperature is an important physical property of seawater, so studying its variation is of great significance. Two kinds of network structures for predicting thermocline time series data are proposed in this paper. One is the LSTM-GRU hybrid neural network model, and the other is the temporal convolutional network (TCN) model. The two networks have obvious advantages over other models in accuracy, stability, and adaptability. Compared with the traditional auto-regressive integrate moving average model, the proposed method considers the influence of temperature history, salinity, depth, and other information. The experimental results show that TCN performs better in prediction accuracy, while LSTM-GRU can better predict abnormal data and has higher robustness.

Keywords

Argo / LSTM / GRU / TCN / marine temperature

References

[1] Zhang Q,Dong J,Sun X.Prediction of sea surface temperature using long short-term memory.IEEE Geosci Remote Sensing Lett2017;14:1745-9

[2] Feder T.Argo begins systematic global probing of the upper oceans.Physics Today2000;53:50-1

[3] Jiang Y,Gou Y,Bai H.High-resolution temperature and salinity model analysis using support vector regression.J Ambient Intell Human Comput2018;

[4] Jiang Y,Zhang T,Hu C.A machine learning approach to Argo data analysis in a thermocline.Sensors (Basel)2017;17:2225 PMCID:PMC5676641

[5] Li X,Zhao M,Jiang Y.Few-shot learning with generative adversarial networks based on WOA13 data.2019;60:1073-85

[6] Jiang Y,Hu C,Bai H.A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU.J Supercomput2019;75:732-45

[7] Xue Y.Forecasts of tropical Pacific SST and sea level using a Markov model.Geophys Res Lett2000;27:2701-4

[8] Landman WA.Forecasts of near-global sea surface temperatures using canonical correlation analysis.J Climate2001;14:3819-33

[9] Penland C.Prediction of Niño 3 sea surface temperatures using linear inverse modeling.J Climate1993;6:1067-76

[10] Johnson SD,Sarachik ES.Empirically derived markov models and prediction of tropical pacific sea surface temperature anomalies*.J Climate2000;13:3-17

[11] Tangang FT,Tang B.Forecasting the equatorial Pacific sea surface temperatures by neural network models.Climate Dynamics1997;13:135-47

[12] Mahongo SB.Using artificial neural networks to forecast monthly and seasonal sea surface temperature anomalies in the Western Indian Ocean.The International Journal of Ocean and Climate Systems2013;4:133-50

[13] Bhaskaran PK,Barman R.A new approach for deriving temperature and salinity fields in the Indian Ocean using artificial neural networks.J Mar Sci Technol2010;15:160-75

[14] Xiao C,Hu C,Gong J.Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach.Remote Sensing of Environment2019;233:111358

[15] Yang Y,Sun X,Mu Q.A CFCC-LSTM model for sea surface temperature prediction.IEEE Geosci Remote Sensing Lett2018;15:207-11

[16] Box G. Time series analysis, forecasting and control. Rev. ed. San Francisco: Holden-Day; c1976.

[17] Zhang G.Time series forecasting using a hybrid ARIMA and neural network model.Neurocomputing2003;50:159-75

[18] Hochreiter S.Long short-term memory.Neural Comput1997;9:1735-80

[19] Cho K,Gulcehre C.Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha: Association for Computational Linguisticsar; 2014. p. 1724-34.

[20] Liu Y,Zhai Z,Zhang B.Timely daily activity recognition from headmost sensor events.ISA Trans2019;94:379-90

[21] Lea C,Vidal R,Hager GD.Temporal convolutional networks for action segmentation and detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017 Jul 21-26. Honolulu: IEEE; 2017. p. 1003-12.

[22] Ergen T.Efficient online learning algorithms based on LSTM neural networks.IEEE Trans Neural Netw Learn Syst2018;29:3772-83

[23] Gers FA,Cummins F.Learning to forget: continual prediction with LSTM.Neural Comput2000;12:2451-71

[24] Rui F,Li L.Using LSTM and GRU neural network methods for traffic flow prediction. 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC); 2016 Nov 11-13. Wuhan: IEEE; 2016. p. 324-8.

[25] Zhang Z,Jiang T,Liu C.Monthly and quarterly sea surface temperature prediction based on gated recurrent unit neural network.JMSE2020;8:249

[26] Hewage P,Trovati M.Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station.Soft Comput2020;24:16453-82