Application of K-Means Clustering and Calendar View Visualisation for Air Pollution Index Analysis

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dc.contributor.authorZ Ali Omar
dc.contributor.authorSiti Rahayu Mohd Hashim
dc.contributor.authorJustin Sentian
dc.contributor.authorSu Na Chin
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-10T03:25:08Z
dc.date.available2024-05-10T03:25:08Z
dc.date.issued2022
dc.description.abstractTwo years of diurnal concentration of particulate matter (PM10) and nitrogen dioxide with the addition of relative humidity measurement, collected from Putrajaya, Malaysia's ground-based measurement station from January 2014 to December 2015, were analysed. K-means clustering was employed and optimal clusters of four were identified for each year based on the most suggested number of clusters from internal cluster validation measures of the total within sum of square, silhouette index and gap statistics. Each cluster was then profiled where each mean pollutant sub-indices were calculated and the contributing pollutant to the air pollution index (API) was determined by looking at the maximum value from all sub-indices. This mechanism closely follows the Recommended Malaysian Air Quality Guidelines (RMG) for determining API.
dc.identifier.citationOmar, Z. A., Hashim, S. R. M., Sentian, J., & Chin, S. N. (2022). Application of K-Means Clustering and Calendar View Visualisation for Air Pollution Index Analysis. In IOP Conference Series: Earth and Environmental Science (Vol. 1103, No. 1, p. 012004). IOP Publishing.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/514
dc.language.isoen
dc.publisherIOP Publishing
dc.publisher.alternativeIOP Conference Series: Earth and Environmental Science
dc.titleApplication of K-Means Clustering and Calendar View Visualisation for Air Pollution Index Analysis
dc.typeJournal

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