Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
2025
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/12791/1/J19437_1856d7f403a3db8ddca45ecbbf6fcbf2.pdf http://eprints.uthm.edu.my/12791/ https://doi.org/10.11591/ijeecs.v37.i2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1836859140964614144 |
|---|---|
| author | Tri Wahyuni, Retno Hanafi, Dirman Tomari, M. Razali Sahid, Sihabudin |
| author_facet | Tri Wahyuni, Retno Hanafi, Dirman Tomari, M. Razali Sahid, Sihabudin |
| author_sort | Tri Wahyuni, Retno |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.uthm.eprints-12791 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my.uthm.eprints-127912025-07-01T23:49:45Z http://eprints.uthm.edu.my/12791/ Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review Tri Wahyuni, Retno Hanafi, Dirman Tomari, M. Razali Sahid, Sihabudin TD Environmental technology. Sanitary engineering 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. 2025 Article PeerReviewed text en http://eprints.uthm.edu.my/12791/1/J19437_1856d7f403a3db8ddca45ecbbf6fcbf2.pdf Tri Wahyuni, Retno and Hanafi, Dirman and Tomari, M. Razali and Sahid, 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 https://doi.org/10.11591/ijeecs.v37.i2 |
| spellingShingle | TD Environmental technology. Sanitary engineering Tri Wahyuni, Retno Hanafi, Dirman Tomari, M. Razali Sahid, Sihabudin Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review |
| title | Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review |
| title_full | Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review |
| title_fullStr | Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review |
| title_full_unstemmed | Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review |
| title_short | Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review |
| title_sort | research trends in spatial modeling of pm2.5 concentration using machine learning: a bibliometric review |
| topic | TD Environmental technology. Sanitary engineering |
| url | http://eprints.uthm.edu.my/12791/1/J19437_1856d7f403a3db8ddca45ecbbf6fcbf2.pdf http://eprints.uthm.edu.my/12791/ https://doi.org/10.11591/ijeecs.v37.i2 |
| url_provider | http://eprints.uthm.edu.my/ |