Chang, Victor;
Ni, Pin;
Li, Yuming;
(2020)
K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index.
IT Professional
, 22
(4)
pp. 28-34.
10.1109/MITP.2020.2993851.
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Abstract
Air pollution has caused environmental and health hazards across the globe, particularly in emerging countries such as China. In this article, we propose the use of air quality index and the development of advanced data processing, analysis, and visualization techniques based on the AI-based k-clustering method. We analyze the air quality data based on seven key attributes and discuss its implications. Our results provide meaningful values and contributions to the current research. Our future work will include the use of advanced AI algorithms and big data techniques to ensure better performance, accuracy and real-time checks.
| Type: | Article |
|---|---|
| Title: | K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/MITP.2020.2993851 |
| Publisher version: | https://doi.org/10.1109/MITP.2020.2993851 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Information Systems, Computer Science, Software Engineering, Telecommunications, Computer Science, PM2.5 CONCENTRATIONS, POLLUTION, NUMBER |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159900 |
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