Bibliometric analysis on machine learning in climate change article during ten years

Abstract

This research used bibliometric method to calculate scientific productivity of corresponding author, first author, affiliation, and correspondence country for machine learning in climate change articles on SCOPUS database from 2015 to 2024. Moreover, spatial simulation of country output is displayed by geographic information system, showing distribution of scientific productivity in each country on the world. Total 4,406 articles are analyzed and simulated, they indicated that research productivity has increasing trend and increases sharply from 2021 to 2024 year with 528 to 1239 articles. Y. Wang correspondence author is 1st ranking and the most research output with 19 articles, including 12 articles in China, 1 article in Finland, and 6 articles in United States. Almost authors publish strongly from 2022 to 2024 with high output as Y. Wang, J. Li, Y. Li, J. Yin, and J. Chen correspondence authors. Y. Zhang first author has the most scientific output with 16 articles and 1st ranking, concluding 13 articles publish in China, 2 articles in Canada, and 1 article in Singapore. Publication of affiliation increases strongly from 2021 to 2024 year and Department of Civil Engineering has the most publication with 97 articles in 20 countries, 1st ranking. China has the most publication in 2015-2024 with 1148 articles, 1st ranking in correspondence author. Publication in countries is increased strongly from 2020 to 2024 year and from 2021-2024, the whole countries have publication at all.