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Air quality prediction using artificial neural network

Ghazali, Suraya and Ismail, Lokman Hakim Air quality prediction using artificial neural network. In: The International Conference on Civil and Environmental Engineering Sustainability (IConCEES 2011), 3-5 April 2012, Johor Bahru, Malaysia.


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Over the last few years, the use of artificial neural networks (ANNs) has increased in many areas of engineering. Artificial neural network have been applied to many environmental engineering problems and have demonstrated some degree of success. The aim of study is to develop neural network air quality prediction model. In this study, a prediction method is developed using feed-forward neural network. Several parameters such as sulphur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), nitric oxide (NO), temperature, relative humidity and air velocity are considered in this study. The performance of the developed model was assessed through a measure of Mean Square Error (MSE) and value of R2. From the constructed networks, the best prediction performance was observed in a model with network structure 7-20-4 with R2 value of 0.57 and MSE 0.062.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: air quality; artificial neural network; prediction; MATLAB
Subjects: T Technology > TD Environmental technology. Sanitary engineering > TD881-890 Air pollution and its control
Depositing User: Normajihan Abd. Rahman
Date Deposited: 27 Jun 2012 07:13
Last Modified: 27 Jun 2012 07:13
URI: http://eprints.uthm.edu.my/id/eprint/2528
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