A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning
Yükleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Machine learning, Climate change, Sustainable development, Bibliometric analysis
Kaynak
Heliyon
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
Cilt
9
Sayı
10
