Browsing by Author "Ahmad Zia Ul-Saufie"

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemEmbargo
    A Review of PM10 Concentrations Modelling in Malaysia
    (IOP Publishing, 2020) Wan Nur Shaziayani; Ahmad Zia Ul-Saufie; Zuraira Libasin; Fuziatul Norsyiha Ahmad Shukri; Sharifah Sarimah Syed Abdullah; Norazian Mohamed Noor
    The purpose of predictive modelling is to predict the variable of interest with reasonable precision, and often to assess the contribution of the independent variables to the dependent variable. In this paper, all of the works examined are aimed at predicting concentrations of outdoor PM10 concentrations. The vast majority of the works reported used almost exclusively predictors of the meteorological and source emissions.
  • Thumbnail Image
    ItemEmbargo
    Coupling of quantile regression into boosted regression trees (BRT) technique in forecasting emission model of PM10 concentration
    (Springer, 2021) Wan Nur Shaziayani; Ahmad Zia Ul-Saufie; Hasfazilah Ahmat; Dhiya Al-Jumeily
    Air pollution is currently becoming a significant global environmental issue. The sources of air pollution in Malaysia are mobile or stationary. Motor vehicles are one of the mobile sources. Stationary sources originated from emissions caused by urban development, quarrying and power plants and petrochemical. The most noticeable contaminant in the Peninsular of Malaysia is the particulate matter (PM10), the highest contributor of Air Pollution Index (API) compared to other pollution parameters. The aim of this study is to determine the best loss function between quantile regression (QR) and ordinary least squares (OLS) using boosted regression tree (BRT) for the prediction of PM10 concentration in Alor Setar, Klang and Kota Bharu, Malaysia.
  • Thumbnail Image
    ItemEmbargo
    Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)
    (Elsevier, 2013) Ahmad Zia Ul-Saufie; Ahmad Shukri Yahaya; Nor Azam Ramli; Norrimi Rosaida; Hazrul Abdul Hamid
    Future PM10 concentration prediction is very important because it can help local authorities to enact preventative measures to reduce the impact of air pollution. The aims of this study are to improve prediction of Multiple Linear Regression (MLR) and Feedforward backpropagation (FFBP) by combining them with principle component analysis for predicting future (next day, next two-day and next three-day) PM10 concentration in Negeri Sembilan, Malaysia.
  • Thumbnail Image
    ItemEmbargo
    Spatial and Temporal Analysis of Particulate Matter (PM10) in Urban-Industrial Environment during Episodic Haze Events in Malaysia
    (Thai Society of Higher Eduation Institutes on Environment, 2023) Izzati Amani Mohd Jafri; Norazian Mohamed Noor; Nur Alis Addiena A Rahim; Ahmad Zia Ul-Saufie; Zulkarnain Hassan; György Deak
    Haze episode in Malaysia typically takes place during the dry monsoon season. As a result, high concentration of atmospheric particles was recorded primarily brought by transboundary air pollution from the neighbour country. Therefore, this study aims to evaluate and compare the level of particulate matter (PM10) at urban-industrial areas during the episodic haze episodes in Malaysia