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

Climate change (CC) is one of the greatest threats to human health, safety, and environment. Given its current future impacts, numerous studies have employed computational tools (e.g., machine learning, ML) understand, mitigate, adapt CC. Therefore, this paper seeks comprehensively analyze research/publications landscape on MLCC research based published documents from Scopus. The high productivity impact has produced highly cited works categorized as science, technology, engineering arts, humanities, social sciences. most prolific author Shamsuddin Shahid (based at Universiti Teknologi Malaysia), whereas Chinese Academy Sciences productive affiliation research. influential countries are United States China, which attributed funding activities National Science Foundation Natural China (NSFC), respectively. Collaboration through co-authorship in high-impact journals such Remote Sensing was also identified an important factor rate among active stakeholders researching topics worldwide. Keyword co-occurrence analysis four major hotspots/themes that describe ML techniques, potential risky sectors, remote sensing, sustainable development dynamics In conclusion, finds a significant socio-economic, environmental, impact, points increased discoveries, publications, citations near future.