Abstract
Respirable particulate matter may cause serious health problems, hence as a precaution, the authorities should take appropriate action to control pollution. An accurate forecast of future particulate matter concentration is important as it can assist the authorities to implement appropriate laws and regulations to address the impact of air pollution. The aim of this study is to investigate the best statistical method for analysing and forecasting particulate matter concentration in Pasir Gudang, Johor, Malaysia. Three statistical approaches are considered, namely Multiple Linear Regression, Principal Component Regression and Time Series Analysis to model and forecast the daily maximum particulate matter concentration levels. The performance of these models is evaluated based on the root mean square error, mean absolute error and mean absolute percentage error. The findings show that Multiple Linear Regression is the best fitted model in forecasting future concentrations, followed by Principal Component Regression, while the time series model produces the least accurate forecasts.
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The authors would like to thank the Department of Environment, Malaysia for providing the data for this study.
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Zulkifli, N.M., Ezzadin, N.W.S., Awang, N. (2022). Performance of Several Statistical Methods in Forecasting Particulate Matter Concentrations in Pasir Gudang, Johor. In: Abdul Karim, S.A. (eds) Intelligent Systems Modeling and Simulation II. Studies in Systems, Decision and Control, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-031-04028-3_31
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DOI: https://doi.org/10.1007/978-3-031-04028-3_31
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