首页|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

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

扫码查看
原文链接
  • 万方数据
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.

forecastingsurface currentsensemble machine learninghigh frequency radarrandom forestAdaBoost

Lei Ren、Lingna Yang、Yaqi Wang、Peng Yao、Jun Wei、Fan Yang、Fearghal O'Donncha

展开 >

School of Ocean Engineering and Technology,Sun Yat-sen University,and Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519082,China

Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-sen University),Ministry of Education,Zhuhai 519082,China

The National Key Laboratory of Water Disaster Prevention,Hohai University,Nanjing 210024,China

School of Atmospheric Sciences,Sun Yat-sen University,Zhuhai 519082,China

Zhuhai Marine Environmental Monitoring Central Station of the State Oceanic Administration,Zhuhai 519082,China

International Business Machines Corporation Research,Dublin D15 HN66,Ireland

展开 >

2024

10.1007/s13131-024-2363-2

海洋学报(英文版)
中国海洋学会

海洋学报(英文版)

CSTPCD
影响因子：0.323
ISSN：0253-505X
年,卷(期)：2024.43(10)