Browsing by Author "Fazureen Azaman"

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    Prediction of the level of air pollution using principal component analysis and artificial neural network techniques: A case study in Malaysia
    (Springer, 2014) Azman Azid; Hafizan Juahir; Mohd Ekhwan Toriman; Mohd Khairul Amri Kamarudin; Ahmad Shakir Mohd Saudi; Che Noraini Che Hasnam; Nor Azlina Abdul Aziz; Fazureen Azaman; Mohd Talib Latif; Syahrir Farihan Mohamed Zainuddin; Mohamad Romizan Osman; Mohammad Yamin
    This study focused on the pattern recognition of Malaysian air quality based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations in Malaysia for 7 years (2005-2011) were gathered. Principal component analysis (PCA) in the environmetric approach was used to identify the sources of pollution in the study locations. The combination of PCA and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API). The PCA has identified that CH4, NmHC, THC, O3, and PM10 are the most significant parameters.