Paper Info
Title
Predicting Sea Surface Temperature Based on a Parallel Autoreservoir Computing Approach With Short-Term Measured Data
Abstract
Sea surface temperature (SST) is an essential parameter for observing the marine environment. It directly reflects the state of heat storage and release in the ocean. The change in SST will cause many phenomena that profoundly affect human production and life. Therefore, predicting SST accurately and efficiently can help us avoid many risks. In this article, we present a method based on neural networks. First, we propose a finite-dimensional description of SST, which investigates SST in the finite-dimensional phase space. Based on phase space reconstruction technology and the autoreservoir neural network (ARNN), the Spatial Parallel ARNN (SPARNN) is proposed for predicting SST. Unlike the previous machine learning methods that require big data, our approach only needs a small amount of locally short-term data to catch the dynamic features of the SST field. It also has excellent parallelism and is easy to run on a large-scale computer platform.
Year
DOI
Venue
2022
10.1109/LGRS.2022.3167408
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Ocean temperature, Sea surface, Temperature measurement, Mathematical models, Temperature distribution, Neural networks, Surface reconstruction, Data-driven method, phase space reconstruction, reservoir computing, sea surface temperature (SST)
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Yin Wang100.34
Shutang Liu25111.49