Yaojian Zhou, Yonglai Zhang, Wenai Song, Shijie Liu, Baoqiang Tian. A hybrid forecasting model for depth-averaged current velocities of underwater gliders[J]. Acta Oceanologica Sinica.
Citation:
Yaojian Zhou, Yonglai Zhang, Wenai Song, Shijie Liu, Baoqiang Tian. A hybrid forecasting model for depth-averaged current velocities of underwater gliders[J]. Acta Oceanologica Sinica.
Yaojian Zhou, Yonglai Zhang, Wenai Song, Shijie Liu, Baoqiang Tian. A hybrid forecasting model for depth-averaged current velocities of underwater gliders[J]. Acta Oceanologica Sinica.
Citation:
Yaojian Zhou, Yonglai Zhang, Wenai Song, Shijie Liu, Baoqiang Tian. A hybrid forecasting model for depth-averaged current velocities of underwater gliders[J]. Acta Oceanologica Sinica.
Software School, North University of China, Taiyuan 030051, China
2.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.
School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Funds:
The National Natural Science Foundation of China under contract Nos U1709202 and 51809127; the Natural Science Foundation of Shanxi Province, China under contract No. 201901D211248.
In this paper, we propose a hybrid forecasting model to improve the forecasting accuracy for depth-averaged current velocities (DACVs) of underwater gliders. The hybrid model is based on a discrete wavelet transform (DWT), a deep belief network (DBN), and a least squares support vector machine (LSSVM). The original DACV series are first decomposed into several high- and one low-frequency subseries by DWT. Then, DBN is used for high-frequency component forecasting, and the LSSVM model is adopted for low-frequency subseries. The effectiveness of the proposed model is verified by two groups of DACV data from sea trials in the South China Sea. Based on four general error criteria, the forecast performance of the proposed model is demonstrated. The comparison models include some well-recognized single models and some related hybrid models. The performance of the proposed model outperformed those of the other methods indicated above.
Figure 2. Structure diagram of the hybrid forecasting model.
Figure 3. DACVs of the two underwater gliders Glider J019 (a) and Glider J021 (b) derived from sea trials in the South China Sea from mid-July to late September 2019.
Figure 4. Magnitude of DACVs of the two underwater gliders Glider J019 (a) and Glider J021 (b).
Figure 5. The DWT decomposition results of DACVs derived from underwater glider J019 (July 14–September 25, 2019).
Figure 6. The forecasting results of DACVs derived from the two underwater gliders Glider J019 (a) and Glider J021 (b).
Figure 7. Forecasting errors of DACVs derived from the two underwater gliders Glider J019 (a) and Glider J021 (b).
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