Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia

Azid, Azman and Juahir, Hafizan and Toriman, Mohd Ekhwan and Kamarudin, Mohd Khairul Amri and Abdul Aziz, Nor Azlina (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. pp. 2063-2076. ISSN 0049-6979, 1573-2932 (Online)

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Abstract

This study focused on the pattern recognition of Malaysian air quality based on the data obtained from theMalaysian 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. The PCAANN showed better predictive ability in the determination of API with fewer variables, with R2 and root mean square error (RMSE) values of 0.618 and 10.017,

Item Type: Article
Additional Information: The authors acknowledge the Air Quality Division of the Department of Environment (DOE) under the Ministry of Natural Resource and Environment, Malaysia, for giving us permission to utilize air quality data, advice, guidance, and support for this study.
Keywords: Environmetric . Pattern recognition . Principal component analysis . Artificial neural network
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Faculty / Institute: Faculty of Bioresources & Food Industry
Depositing User: Professor Mohd Ekhwan Toriman
Date Deposited: 16 Nov 2014 03:18
Last Modified: 16 Nov 2014 03:18
URI: http://erep.unisza.edu.my/id/eprint/1494

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