Journal Articles

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https://hdl.handle.net/20.500.14170/296

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Browsing Journal Articles by Author "Ahmad Zia Ul Saufie"
  • Publication
    Prediction of particulate matter (PM₁₀) during high particulate event in peninsular Malaysia using Novel Hybrid Model
    (EDP Sciences, 2023)
    Izzati Amani Mohd Jafri
    ;
    Norazian Mohamed Noor
    ;
    Nur Alis Addiena A Rahim
    ;
    Ahmad Zia Ul Saufie
    ;
    György Deak Habil
    High Particulate Events (HPE) contributes to the deterioration of air quality, as the fine particles present can be inhaled, leading to respiratory diseases and other health problem. Knowing the adverse effects of air pollution episodes to human health, it is crucial to create suitable models that can effectively and accurately predict air pollution concentration. This study proposed a hybrid model for forecasting the next day PM₁₀ concentration in peninsular Malaysia namely Shah Alam, Nilai, Bukit Rambai and Larkin. Hourly air pollutant concentration (PM₁₀, NOx, NO₂, SO₂, CO, O₃) and meteorological parameters (RH, T, WS) during the HPE events in 1997, 2005, 2013 and 2015 were used. Support Vector Machine (SVM) and Quantile Regression (QR) was combined to construct a hybrid models (SVM-QR) to reduce the number of input variables. Performance indicators such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Index of Agreement (d2) were used to evaluate the performance of the predictive models. SVM-QR model resulted good performance in all areas. SVM-3 was selected as the best model at Bukit Rambai (MAE=5.72, RMSE=9.71) and Shah Alam (MAE=11.89, RMSE=22.66), while SVM-1 as the best model at Larkin and Nilai with the value (MAE=7.22, RMSE=13.38) and (MAE=6.88, RMSE=11.84), respectively. This strategy was proven to help reducing the complexity of the model and enhance the predictive capacity of the model.
  • Publication
    Prediction of PM₁₀ level during high particulate event in Malaysia using modified model
    (EDP Sciences, 2023)
    Nur Alis Addiena A Rahim
    ;
    Norazian Mohamed Noor
    ;
    Izzati Amani Mohd Jafri
    ;
    Ahmad Zia Ul Saufie
    ;
    Boboc Madalina
    Particulate matter (PM10) is one of the key indicator of air quality index (API) during high particulate event (HPE). PM10 can cause adverse effect on human health and environment; hence, it is important to develop a reliable and accurate predictive model to be used as forecasting tool to alarm the citizen especially during HPE. This study aims to develop a modified Quantile Regression (QR) model to forecast the PM10 concentration during HPE in Malaysia. The performances of three predictive models namely Multiple Linear Regression (MLR), Quantile Regression (QR) and a modified QR models i.e. combination of QR with Relief-based were compared. The hourly dataset of PM10 concentration with other gaseous pollutants and weather parameters at Klang from the year with severe haze event in Malaysia (1997, 2005, 2013 and 2015) were obtained from Department of Environment (DOE) Malaysia. Three performance measures namely Mean Absolute Error (MAE), Normalised Absolute Error (NAE) and Root Mean Squared Error (RMSE) were calculated to evaluate the accuracy of the predictive models. This study found that the Relief-QR model showed the best performance compared to MLR and QR models. The prediction of future PM10 concentration is very important because it can aid the local authorities to implement precautionary measures to limit the impact of air pollution.