Prediction of the level of air pollution using principal component analysis and artificial neural network techniques

A case study in Malaysia

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

Research output: Contribution to journalArticle

53 Citations (Scopus)

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.

Original languageEnglish
Article number2063
JournalWater, Air, and Soil Pollution
Volume225
Issue number8
DOIs
Publication statusPublished - 2014

Fingerprint

Air pollution
Principal component analysis
artificial neural network
principal component analysis
atmospheric pollution
Neural networks
Air Pollutants
Sampling
prediction
Air quality
air quality
sampling
Dronabinol
pattern recognition
Air
Mean square error
Pattern recognition
Pollution
Monitoring
cost

Keywords

  • Artificial neural network
  • Environmetric
  • Pattern recognition
  • Principal component analysis

ASJC Scopus subject areas

  • Pollution
  • Environmental Chemistry
  • Environmental Engineering
  • Ecological Modelling
  • Water Science and Technology

Cite this

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.

In: Water, Air, and Soil Pollution, Vol. 225, No. 8, 2063, 2014.

Research output: Contribution to journalArticle

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. In: Water, Air, and Soil Pollution. 2014 ; Vol. 225, No. 8.
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