Forecasting of surface current velocities using ensemble machine learning algorithms for the Guangdong−Hong Kong−Macao Greater Bay area based on the high frequency radar data

Lei Ren; Lingna Yang; Yaqi Wang; Peng Yao; Jun Wei; Fan Yang; Fearghal O’ Donncha

doi:10.1007/s13131-024-2363-2

Volume 43 Issue 10

Oct. 2024

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Lei Ren, Lingna Yang, Yaqi Wang, Peng Yao, Jun Wei, Fan Yang, Fearghal O’ Donncha. Forecasting of surface current velocities using ensemble machine learning algorithms for the Guangdong−Hong Kong−Macao Greater Bay area based on the high frequency radar data[J]. Acta Oceanologica Sinica, 2024, 43(10): 1-15. doi: 10.1007/s13131-024-2363-2

Citation:

Lei Ren, Lingna Yang, Yaqi Wang, Peng Yao, Jun Wei, Fan Yang, Fearghal O’ Donncha. Forecasting of surface current velocities using ensemble machine learning algorithms for the Guangdong−Hong Kong−Macao Greater Bay area based on the high frequency radar data[J]. Acta Oceanologica Sinica, 2024, 43(10): 1-15. doi: 10.1007/s13131-024-2363-2

Citation:

Lei Ren, Lingna Yang, Yaqi Wang, Peng Yao, Jun Wei, Fan Yang, Fearghal O’ Donncha. Forecasting of surface current velocities using ensemble machine learning algorithms for the Guangdong−Hong Kong−Macao Greater Bay area based on the high frequency radar data[J]. Acta Oceanologica Sinica, 2024, 43(10): 1-15. doi: 10.1007/s13131-024-2363-2

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Forecasting of surface current velocities using ensemble machine learning algorithms for the Guangdong−Hong Kong−Macao Greater Bay area based on the high frequency radar data

doi: 10.1007/s13131-024-2363-2

1.
School of Ocean Engineering and Technology, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
2.
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
3.
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210024, China
4.
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
5.
Zhuhai Marine Environmental Monitoring Central Station of the State Oceanic Administration, Zhuhai 519082, China
6.
International Business Machines Corporation Research, Dublin D15 HN66, Ireland

Funds: The fund from Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. SML2020SP009; the National Basic Research and Development Program of China under contract Nos 2022YFF0802000 and 2022YFF0802004; the “Renowned Overseas Professors” Project of Guangdong Provincial Department of Science and Technology under contract No. 76170-52910004; the Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention under contract No. 2022491711; the National Natural Science Foundation of China under contract No. 51909290; the Key Research and Development Program of Guangdong Province under contract No. 2020B1111020003.

More Information

Corresponding author: E-mail: weijun5@mail.sysu.edu.cn
Received Date: 2023-12-15
Accepted Date: 2024-04-25

Available Online: 2024-08-07

Publish Date: 2024-10-01

Abstract

Abstract

Forecasting of ocean currents is critical for both marine meteorological research and ocean engineering and construction. Timely and accurate forecasting of coastal current velocities offers a scientific foundation and decision support for multiple practices such as search and rescue, disaster avoidance and remediation, and offshore construction. This research established a framework to generate short-term surface current forecasts based on ensemble machine learning trained on high frequency radar observation. Results indicate that an ensemble algorithm that used random forests to filter forecasting features by weighting them, and then used the AdaBoost method to forecast can significantly reduce the model training time, while ensuring the model forecasting effectiveness, with great economic benefits. Model accuracy is a function of surface current variability and the forecasting horizon. In order to improve the forecasting capability and accuracy of the model, the model structure of the ensemble algorithm was optimized, and the random forest algorithm was used to dynamically select model features. The results show that the error variation of the optimized surface current forecasting model has a more regular error variation, and the importance of the features varies with the forecasting time-step. At ten-step ahead forecasting horizon the model reported root mean square error, mean absolute error, and correlation coefficient by 2.84 cm/s, 2.02 cm/s, and 0.96, respectively. The model error is affected by factors such as topography, boundaries, and geometric accuracy of the observation system. This paper demonstrates the potential of ensemble-based machine learning algorithm to improve forecasting of ocean currents.
- forecasting,
- surface currents,
- ensemble machine learning,
- high frequency radar,
- random forest,
- AdaBoost

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References(47)

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