Browsing by Author "Norhashidah Awang"

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    Bayesian autoregressive spatiotemporal model of PM10 concentrations across Peninsular Malaysia
    (Springer, 2018) Edna Manga; Norhashidah Awang
    Rapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM10 are typically variable in space and time. The increased vigilance of policy makers in monitoring pollutant levels has led to vast amounts of spatiotemporal data available for modelling and inference. The aim of this study is to model and predict the spatiotemporal daily PM10 levels across Peninsular Malaysia. A hierarchical autoregressive spatiotemporal model is applied to daily PM10 concentration levels from thirty-four monitoring stations in Peninsular Malaysia during January to December 2011.
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    Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia
    (Elsevier, 2018) Kar Yong Ng; Norhashidah Awang
    Frequent haze occurrences in Malaysia have made the management of PM10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM10 variation and good forecast of PM10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants.
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    Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia
    (Springer, 2019) Ng Kar Yong; Norhashidah Awang
    This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy.The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT.