Analyzing PM2.5 Levels in Indonesia Using Dynamic Time Warping and Fuzzy Clustering: A Time-Series Ecological Study
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Abstract
Air pollution from particulate matter (PM2.5) poses serious global health risks. This study evaluated five fuzzy clustering methods Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), Possibilistic Fuzzy C-Means (PFCM), Fuzzy Gustafson-Kessel (FGK), and Fuzzy C-Shells (FCS)—integrated with Dynamic Time Warping (DTW) to cluster PM2.5 levels across 33 Indonesian capital cities. Using an ecological time series design, daily PM2.5 data (March 6, 2023–March 5, 2024; 12,045 data points) from PlumeLabs were analyzed in R 4.4.2 with descriptive and inferential statistics, including the Kolmogorov-Smirnov, Kruskal-Wallis, and Dunn tests. Three clusters—high, moderate, and low pollution—were identified. The DTW+FCS method showed the best performance (PCI: 0.798, MPCI: 0.697, PEI: 0.357, XBI: 0.197) with significant differences (𝑝 < 0.000). These findings highlight DTW+FCS as the optimal approach and emphasize targeted air quality strategies for Indonesia’s highpollution areas.
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