Sea surface temperature prediction model based on long and short-term memory neural network

被引：6
|
作者：
Li, Xiaojing [1 ]
机构：
[1] Guangxi Coll Presch Educ, Nanning 530022, Peoples R China
来源：
3RD INTERNATIONAL FORUM ON GEOSCIENCE AND GEODESY | 2021年 / 658卷
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1088/1755-1315/658/1/012040
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
In response to the monitoring and forecasting of El Nino/La Nina phenomenon, This paper proposes a sea surface temperature prediction method based on long and short-term memory neural network for the average sea surface temperature in the NINO comprehensive area. This method uses the monthly anomaly sea surface temperature sequence for the mean sea surface temperature in the NINO Comprehensive area as the input of the long- and short-term memory neural network to establish a forecast model. The average sea surface temperature of the NINO comprehensive area is forecasted for the next 1 to 3 months. The results show that the method can better predict the average sea surface temperature of the NINO comprehensive area, which is useful for the monitoring and forecasting of El Nino/La Nina phenomenon. Provides a new approach.
引用
收藏
页数：5
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