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

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorZaidi, Abdelhamid
dc.contributor.authorBekun, Festus Victor
dc.contributor.authorAdediran, Anthonia Oluwatosin
dc.contributor.authorBassey, Mbiatke Anthony
dc.date.accessioned2023-11-13T11:19:19Z
dc.date.available2023-11-13T11:19:19Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractClimate 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.en_US
dc.identifier.doi10.1016/j.heliyon.2023.e20297en_US
dc.identifier.issue10en_US
dc.identifier.pmid37780782en_US
dc.identifier.scopus2-s2.0-85172181455en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/7019
dc.identifier.urihttps://doi.org/10.1016/j.heliyon.2023.e20297
dc.identifier.volume9en_US
dc.identifier.wosWOS:001085356000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofHeliyonen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine learning, Climate change, Sustainable development, Bibliometric analysisen_US
dc.titleA research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learningen_US
dc.typeArticleen_US

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