Browsing by Author "Nor Azlina Abdul Aziz"

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    Analysis of meander evolution studies on effect from land use and climate change at the upstream reach of the Pahang River, Malaysia
    (Springer, 2015) Mohd Khairul Amri Kamarudin; Mohd Ekhwan Toriman; Mohd Hafiz Rosli; Hafizan Juahir; Nor Azlina Abdul Aziz; Azman Azid; Syahrir Farihan Mohamed Zainuddin; Wan Nor Azmin Sulaiman
    Hydrogeomorphologically, the study of river meandering provides information on the tendency of rivers to reach and form a state of equilibrium. The process of meander changes is important in order to identify the environment-related causes that occur naturally or vice versa. Sedimentation, erosion, flood, and water quality problems usually are being specifically studied, but in a broad view, changes in the platform of the river affect all the problems that occur. This article discusses the effects of the meanders evolution changes from land use and climate change in the upstream of Sungai Pahang in over 61 years from 1932 to 1993.
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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.