卷积神经网络在近岸表层海温预报中的应用

doi:10.11978/2023037

Abstract

Abstract:

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.

Key words: sea surface temperature, near-shore stations, multivariate time series, CNN

WENG Shaojia, CAI Jinhai, PANG Yunxi, LUO Rongzhen. Application of convolutional neural network to sea surface temperature prediction in the coastal waters[J].Journal of Tropical Oceanography, 2024, 43(1): 40-47.

Figures/Tables 6

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References 20

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月份	日最高SST/ ℃						日最低SST/ ℃						日平均SST/ ℃
	CNN预报		LSTM预报		人工经验预报		CNN预报		LSTM预报		人工经验预报		CNN预报		LSTM预报
	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
1月	0.21	0.28	0.22	0.30	0.24	0.31	0.17	0.23	0.15	0.21	0.30	0.37	0.14	0.22	0.11	0.19
2月	0.31	0.51	0.30	0.50	0.36	0.59	0.19	0.26	0.17	0.25	0.29	0.37	0.18	0.24	0.15	0.21
3月	0.35	0.44	0.40	0.53	0.40	0.53	0.18	0.25	0.17	0.24	0.29	0.37	0.18	0.25	0.19	0.28
4月	0.35	0.45	0.33	0.43	0.40	0.57	0.27	0.38	0.26	0.36	0.31	0.46	0.26	0.37	0.27	0.38
5月	0.48	0.60	0.50	0.63	0.53	0.64	0.39	0.50	0.42	0.52	0.47	0.65	0.39	0.49	0.33	0.46
6月	0.54	0.67	0.49	0.62	0.91	1.15	0.79	1.13	0.74	1.15	1.28	1.78	0.54	0.74	0.54	0.71
7月	0.63	0.80	0.61	0.76	0.88	1.06	1.12	1.45	1.05	1.41	1.36	1.75	0.78	0.97	0.77	0.92
8月	0.41	0.52	0.41	0.51	0.68	0.83	0.45	0.72	0.44	0.71	0.62	0.85	0.32	0.41	0.34	0.44
9月	0.33	0.48	0.38	0.51	0.53	0.78	0.33	0.47	0.37	0.51	0.48	0.72	0.32	0.44	0.30	0.46
10月	0.22	0.29	0.21	0.26	0.46	0.56	0.08	0.13	0.10	0.15	0.20	0.30	0.12	0.16	0.18	0.22
11月	0.24	0.30	0.26	0.31	0.34	0.45	0.14	0.19	0.14	0.19	0.35	0.56	0.15	0.20	0.17	0.24
12月	0.19	0.23	0.20	0.26	0.30	0.40	0.21	0.29	0.24	0.33	0.38	0.46	0.17	0.25	0.18	0.24
全年	0.36	0.49	0.36	0.49	0.50	0.70	0.36	0.63	0.36	0.63	0.53	0.87	0.30	0.47	0.30	0.45

Application of convolutional neural network to sea surface temperature prediction in the coastal waters

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