Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping
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Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
2
Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editors: Rezzy Eko Caraka, Youngjo Lee, Toni Toharudin, Rung-Ching Chen, Heri Kuswanto and Maengseok Noh
Atmosphere 2022, 13(4), 503; https://doi.org/10.3390/atmos13040503
Received: 27 January 2022
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Revised: 18 March 2022
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Accepted: 21 March 2022
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Published: 22 March 2022
(This article belongs to the Special Issue Recent Topics of Climate Vulnerability: Statistics, Machine Learning, and Data Science, from Theory to Application)
Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns. The identified clusters were analyzed to gain meaningful information regarding air quality patterns in Malaysia and to identify characterization for each cluster. PM10 time series data from 5 July 2017 to 31 January 2019, obtained from the Malaysian Department of Environment and Dynamic Time Warping as the dissimilarity measure were used in this study. At the same time, k-Means, Partitioning Around Medoid, agglomerative hierarchical clustering, and Fuzzy k-Means were the algorithms used for clustering. The results portray that the categories and activities of locations of the monitoring stations do not directly influence the pattern of the PM10 values, instead, the clusters formed are mainly influenced by the region and geographical area of the locations.
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Keywords:
air quality; time series clustering; Dynamic Time Warping; k-Means; Partitioning Around Medoid; agglomerative hierarchical clustering; Fuzzy k-Means
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MDPI and ACS Style
Suris, F.N.A.; Bakar, M.A.A.; Ariff, N.M.; Mohd Nadzir, M.S.; Ibrahim, K. Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping. Atmosphere 2022, 13, 503. https://doi.org/10.3390/atmos13040503
AMA Style
Suris FNA, Bakar MAA, Ariff NM, Mohd Nadzir MS, Ibrahim K. Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping. Atmosphere. 2022; 13(4):503. https://doi.org/10.3390/atmos13040503
Chicago/Turabian StyleSuris, Fatin Nur Afiqah, Mohd Aftar Abu Bakar, Noratiqah Mohd Ariff, Mohd Shahrul Mohd Nadzir, and Kamarulzaman Ibrahim. 2022. "Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping" Atmosphere 13, no. 4: 503. https://doi.org/10.3390/atmos13040503
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