RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction

For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospheric reflection which can be used to predict SST var...

Full description

Saved in:

Bibliographic Details
Published in:	EURASIP journal on wireless communications and networking Vol. 2021; no. 1; pp. 1 - 18
Main Authors:	Zhu, Linqian, Liu, Qi, Liu, Xiaodong, Zhang, Yonghong
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 26.08.2021 Springer Nature B.V SpringerOpen
Subjects:	Accuracy Adaptability Algorithms Atmospheric reflection Communications Engineering Emission analysis Engineering Environmental protection Global warming Greenhouse gases Grey model Industrial development Information Systems Applications (incl.Internet) Long-term prediction Mathematical analysis Methods Model accuracy Multi-modal Sensor Data Fusion in Internet of Things Natural resources Networks Neural networks Precipitation Radiation Regional SST Sea surface temperature Seawater Signal,Image and Speech Processing Simulation Temperature variation Trends Wireless networks Regional SST Atmospheric reflection Grey model Long-term prediction Temperature variation
ISSN:	1687-1499, 1687-1472, 1687-1499
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!