A bibliometric analysis of deep learning applications in climate change research using CiteSpace
Abstract
In recent years, artificial intelligence, particularly deep learning, has garnered significant attention among practitioners and scholars in meteorology and atmospheric sciences, leading to a substantial body of literature. This study aims to delineate the present research status and trends in climate innovation through CiteSpace visual analysis. To comprehend the current landscape, prevalent terms, and research frontiers of deep learning for climate change research (DLCCR) within meteorology and atmospheric applications, we gathered 256 published papers spanning from 2018 to 2022 from the Web of Science (WOS) core database. Employing these articles, we conducted co-authorship, co-citation, and keyword co-occurrence analyses. The findings unveiled a steady rise in DLCCR publications over the last five years. However, the correlation between high yield and high-citation authorship appears inconsistent and weak. Notably, prolific authors in this domain included Zhang Z.L. and Bonnet P. Furthermore, leading institutions such as the Chinese Academy of Sciences (China), le Centre National de la Recherche Scientifique (France), and Nanjing University of Information Science and Technology (China) have played pivotal roles in advancing DLCCR. The primary contributors among high-yield countries primarily cluster in a select group comprising China, the USA, South Korea, and Germany. Identifying significant information gaps in numerical weather, atmospheric physics and processes, algorithm parametrizations, and extreme events, our study underscores the necessity for future researchers to focus on these and related subjects. This study provides valuable insights into research hotspots, developmental trajectories, and emerging frontiers, thereby delineating the knowledge structure in this field and highlighting directions for further climate innovation research.
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DOI: http://dx.doi.org/10.18686/jaoe.v11i1.10456
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