Azman AzidHafizan JuahirMohd Ekhwan TorimanMohd Khairul Amri KamarudinAhmad Shakir Mohd SaudiChe Noraini Che HasnamNor Azlina Abdul AzizFazureen AzamanMohd Talib LatifSyahrir Farihan Mohamed ZainuddinMohamad Romizan OsmanMohammad Yamin2024-05-102024-05-102014Azid, A., Juahir, H., Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M., Hasnam, C. N. C., Aziz, N.A.A., Azaman, F., Latif, M.T., Zainuddin, S.F.M., Osman, M.R., & Yamin, M. (2014). Prediction of the level of air pollution using principal component analysis and artificial neural network techniques: A case study in Malaysia. Water, Air, & Soil Pollution, 225, 1-14.https://repoemc.ukm.my/handle/123456789/602This 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.EnvironmetricPattern recognitionPrincipal component analysisArtificial neural networkJournal