Deep learning for sea surface temperature applications: A comprehensive bibliometric analysis and methodological approach

This study explored the potential application of deep learning techniques in sea surface temperature (SST) investigations using a mixed method, bibliometric analysis and methodological approach. CiteSpace software was utilized for a bibliometric study on 137 academic publications from 2018 to 2023....

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Vydáno v:Geo : geography and environment Ročník 11; číslo 2
Hlavní autoři: Boufeniza, Redouane Larbi, Jingjia, Luo, Abdela, Kemal Adem, Alsafadi, Karam, Alsahli, Mohammad M
Médium: Journal Article
Jazyk:angličtina
Vydáno: London John Wiley & Sons, Inc 01.07.2024
Wiley
Témata:
Algorithms
Artificial intelligence
Bibliometrics
CiteSpace
Computer science
Deep learning
Geography
Keywords
Knowledge
marine environment
meteorology
Methods
R&D
Remote sensing
Research & development
research hotspots
Scientists
Scientometrics
Sea surface temperature
SST
Thermal pollution
Trends
Visualization
ISSN:2054-4049, 2054-4049
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Shrnutí:This study explored the potential application of deep learning techniques in sea surface temperature (SST) investigations using a mixed method, bibliometric analysis and methodological approach. CiteSpace software was utilized for a bibliometric study on 137 academic publications from 2018 to 2023. Various databases were employed for methodological analysis, which involved examining publications based on models, methodologies, applications and research areas. The data were manually organized in a relational framework of an SQL database. The analysis underscored China's prominence as a leader in the extensive research devoted to this field. The United States of America and the United Kingdom played pivotal roles in providing the essential data that served as the foundation for these studies. Moreover, the long short‐term memory (LSTM) algorithm was the predominant computational deep learning algorithm extensively used in this specific context. The analysis highlighted significant knowledge gaps in areas such as SST forecasting, modelling, satellite remote sensing, extreme events and data reconstruction. Future scientists need to show more interest in these and related subjects, while Chinese and American scientists should prioritize paper quality over quantity. Additionally, fostering stronger collaborations between universities and institutions is vital for further advancements. Ultimately, this study offers valuable insights into hotspot research areas and development processes, establishing the foundation for research and suggesting possible avenues for future development. The results of this evaluation serve as an essential guide for researchers and modellers involved in prediction initiatives using deep learning. Short The main objective of this study is to highlight research gaps and provide a comprehensive overview of the most recent trends (last six years) involving deep learning techniques for sea surface temperature (SST) investigations. The study aims to provide information's, including the methodologies, an assessment of strengths and weaknesses in terms of institutions, keywords, authors, journals and collaborative efforts. In addition, the study highlights persistent challenges and potential future directions in SST research using deep learning methods.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2054-4049
2054-4049
DOI:10.1002/geo2.151