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

Azman, Azid and Hafizan, Juahir and Mohd Ekhwan, Toriman (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 (8). ISSN 00496979

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Abstract

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. The PCA-ANN showed better predictive ability in the determination of API with fewer variables, with R 2 and root mean square error (RMSE) values of 0.618 and 10.017, respectively. The work has demonstrated the importance of historical data in sampling plan strategies to achieve desired research objectives, as well as to highlight the possibility of determining the optimum number of sampling parameters, which in turn will reduce costs and time of sampling.

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