Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review

Tri Wahyuni, Retno and Hanafi, Dirman H and Tomari, M. Razali and Sahid, Dadang Syarif Sihabudin (2025) Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review. Indonesian Journal of Electrical Engineering and Computer Science, 37 (2). pp. 1317-1327. ISSN 2502-4752

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Abstract

Spatial modeling is commonly used to map research variables, including particulate matter 2.5 (PM2.5) concentrations, in specific areas. The article that surveys publications on the application of machine learning in spatial modeling of PM2.5 using bibliometric methods has not been identified yet. This paper aims to analyze trends in applying machine learning in the spatial modeling of PM2.5 using bibliometric methods. The review was conducted on publications indexed in the Scopus database over the decade (2014–2023) comprising 335 articles. The analysis included co-authorship and cooccurrence using VOSviewer. From the two stages of analysis, it can be concluded that research on this topic has constantly increased over the past 10 years, with the highest productivity coming from researchers in China. This research topic is multidisciplinary, with most publications appearing in environmental science. The research also shows a very high collaboration rate of 0.98. A deeper examination of the keywords reveals the most commonly used machine learning techniques by researchers. The random forest method is the most frequently found in the analyzed documents, followed by deep learning, long short-term memory (LSTM), extreme gradient boosting (XGBoost), and ensemble model.

Item Type:	Article
Uncontrolled Keywords:	Bibliometric Machine learning PM2.5 Spatial modeling VOSViewe
Subjects:	T Technology > TD Environmental technology. Sanitary engineering
Divisions:	Faculty of Electrical and Electronic Engineering > FKEE
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	29 Aug 2025 00:10
Last Modified:	29 Aug 2025 00:11
URI:	http://eprints.uthm.edu.my/id/eprint/12945

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