Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia-Reference-Cited by-同舟云学术

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

Published:2014-07-21 Issue:8 Volume:225 Page:
ISSN:0049-6979
Container-title:Water, Air, & Soil Pollution
language:en
Short-container-title:Water Air Soil Pollut

Author:

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

Publisher

Springer Science and Business Media LLC

Subject

Pollution,Water Science and Technology,Ecological Modeling,Environmental Chemistry,Environmental Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11270-014-2063-1.pdf

Reference87 articles.

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2. Abdul-Wahab, S. A., Bakheit, C. S., & Al-Alawi, S. M. (2005). Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environmental Modelling and Software, 20, 1263–1271.

3. Afroz, R., Hassan, M. N., & Ibrahim, N. A. (2003). Review of air pollution and health impacts in Malaysia. Environmental Research, 92, 71–77. doi: 10.1016/S0013-9351(02)00059-2 .

4. Afzali, M., Afzali, A., & Zahedi, G. (2012). The potential of artificial neural network technique in daily and monthly ambient air temperature prediction. International Journal of Environmental Science Development, 3(1), 33–38.

5. Alam Sekitar Malaysia Sdn Bhd (ASMA). (2007). Standard operating procedure for continuous air quality monitoring. Selangor Malaysia: Shah Alam.

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