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

Azid, Azman; Juahir, Hafizan; Toriman, Mohd Ekhwan; Kamarudin, Mohd Khairul Amri; Saudi, Ahmad Shakir Mohd; Hasnam, Che Noraini Che; Aziz, Nor Azlina Abdul; Azaman, Fazureen; Latif, Mohd Talib; Zainuddin, Syahrir Farihan Mohamed; Osman, Mohamad Romizan; Yamin, Mohammad

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

2014

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 PM₁₀are the most significant parameters. The PCA-ANN showed better predictive ability in the determination of API with fewer variables, with R²and 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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