Effect of Monsoonal Clustering for PM10 Concentration Prediction in Keningau, Sabah Using Principal Component Analysis
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
IOP Publishing
Abstract
Particulate matter (PM) has caught scientific attention in scientific research due to its harmful effect on human health. While prediction is essential for future development in Keningau, temporal clustering in Keningau has yet to be studied. Thus, this research aims to determine whether monsoonal clustering is required for meteorological and pollutant concentration data collected in Keningau. Missing data is first imputed using Nearest Neighbour Method (NNM). Then, wind direction and wind speed are converted into northern (Wy) and eastern (Wx) component of wind speed. Data is then temporal clustered based on monsoonal season (NEM, IM4, SWM, IM10). Both clustered and unclustered data are analysed using principal component (PC) analysis (PCA).
Description
Keywords
PM10, nearest neighbour method, monsoonal cluster, principal component analysis, regression analysis
Citation
Rumaling, M. I., Chee, F. P., Chang, J. H. W., & Sentian, J. (2022). Effect of Monsoonal Clustering for PM10 Concentration Prediction in Keningau, Sabah using Principal Component Analysis. In IOP Conference Series: Earth and Environmental Science (Vol. 1103, No. 1, p. 012003). IOP Publishing.