Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support

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Abdullah S.
Ismail M.
Ahmed A.N.
Abdullah A.M.
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Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM10) throughout the years. Studies have affirmed that PM10 influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM10 status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000-2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM10 and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM10 compared to the linear model. The results are robust enough for precise next day forecasting of PM10 concentration on the East Coast of Peninsular Malaysia. � 2019 by the authors.
Air quality; Decision support systems; Fog; Forecasting; Functions; Linear regression; Nonlinear systems; Particles (particulate matter); Radial basis function networks; Forecasting algorithm; Forecasting performance; Malaysia; Multi layer perceptron; Multiple linear regressions; Particulate Matter; Radial basis functions; Urban and rural areas; Urban growth; air quality; algorithm; decision support system; early warning system; forecasting method; multiple regression; particulate matter; performance assessment; precision; Malaysia; West Malaysia