基于Stacking机器学习模型的南海北部海温预报-海洋预报

基于Stacking机器学习模型的南海北部海温预报

单位：1. 国家海洋环境预报中心自然资源部海洋灾害预报技术重点实验室, 北京 100081;
2. 厦门大学海洋与地球学院, 福建厦门 361102

分类号：P731.31

出版年·卷·期（页码）：2023·40·第一期（39-45）

摘要：

基于 Stacking（ET-ET）的机器学习算法，利用美国国家环境预报中心再分析数据和MGDSST海温融合数据，建立了一套高效的海温长期预报方法，并在南海北部海域开展了1 a的表层海温长期预报实验。结果表明：基于Stacking（ET-ET）机器学习模型的表层海温长期预报的均方根误差降至0.52 ℃，平均绝对百分比误差降至1.58%，明显优于基于支持向量机、人工神经网络和长短期记忆模型的预报结果。

In this paper, an efficient long-term SST forecast method is established based on Stacking (ET-ET) machine learning algorithm using reanalysis data of National Centers for Environmental Prediction and Mergid satellite and in situ data Global Daily sea surface temperature (SST) fusion data, and long-term SST forecast experiment is carried out in the northern South China Sea for one year. The results show that the root mean square error of long-term SST forecast based on Stacking (ET-ET) machine learning model is reduced to 0.52 ℃, and the mean absolute percentage error is reduced to 1.58%, which is significantly better than the forecast results based on the support vector machine, artificial neural network and long short-term memory model.

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