基于Stacking机器学习模型的南海北部海温预报  

Sea temperature forecast in the northern South China Sea base on Stacking machine learning model

作　　者：孙昭 李云[1] 江毓武[2] 王兆毅[1] SUN Zhao;LI Yun;JIANG Yuwu;WANG Zhaoyi(Key Laboratory of Marine Hazards Forecasting,National Marine Environmental Forecasting Center,Ministry of Natural Resources,Beijing 100081,China;College of Ocean and Earth Sciences,Xiamen University,Xiamen 361102,China)

机构地区：[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

出　　处：《海洋预报》2023年第1期39-45,共7页Marine Forecasts

基　　金：国家重点研发计划(2022YFC3105102)。

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.

关 键 词：机器学习 STACKING 南海北部 海温预报 

分 类 号：P731.31[天文地球—海洋科学]