Artículo

A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning

Resumen

Climate change (CC) is one of the greatest threats to human health, safety, and the environment. Given its current and future impacts, numerous studies have employed computational tools (e.g., machine learning, ML) to understand, mitigate, and adapt to CC. Therefore, this paper seeks to comprehensively analyze the research/publications landscape on the MLCC research based on published documents from Scopus. The high productivity and research impact of MLCC has produced highly cited works categorized as science, technology, and engineering to the arts, humanities, and social sciences. The most prolific author is Shamsuddin Shahid (based at Universiti Teknologi Malaysia), whereas the Chinese Academy of Sciences is the most productive affiliation on MLCC research. The most influential countries are the United States and China, which is attributed to the funding activities of the National Science Foundation and the National Natural Science Foundation of China (NSFC), respectively. Collaboration through co-authorship in high-impact journals such as Remote Sensing was also identified as an important factor in the high rate of productivity among the most active stakeholders researching MLCC topics worldwide. Keyword co-occurrence analysis identified four major research hotspots/themes on MLCC research that describe the ML techniques, potential risky sectors, remote sensing, and sustainable development dynamics of CC. In conclusion, the paper finds that MLCC research has a significant socio-economic, environmental, and research impact, which points to increased discoveries, publications, and citations in the near future.
Ajibade, Samuel-Soma M. (57209028006); Zaidi, Abdelhamid (9241739100); Bekun, Festus Victor (57193455217); Adediran, Anthonia Oluwatosin (57210390374); Bassey, Mbiatke Anthony (57816076400)
A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning
2023
10.1016/j.heliyon.2023.e20297
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172181455&doi=10.1016%2fj.heliyon.2023.e20297&partnerID=40&md5=15952c347a2e0125a45dc711dee8ce34
Department of Computer Engineering, Istanbul Ticaret University, Istanbul, Turkey; Department of Mathematics, College of Science, Qassim University, Qassim, Buraydah, Saudi Arabia; Faculty of Economics Administrative and Social Sciences, Istanbul Gelisim University, Istanbul, Turkey; Adnan Kassar School of Business, Department of Economics, Lebanese American University, Beirut, Lebanon; Faculty of Architecture and Urban Design, Federal University of Uberlandia, Minas Gerais, Brazil; Department of Estate Management, The Federal Polytechnic, Ado Ekiti, Nigeria; Department of Business Administration, UTHM, Batu Pahat, Malaysia
All Open Access; Gold Open Access; Green Open Access
Scopus
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