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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-08

AUTHORS

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

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 R2 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. More... »

PAGES

2063

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11270-014-2063-1

DOI

http://dx.doi.org/10.1007/s11270-014-2063-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038466283


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