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Air pollution index prediction using multiple neural net

Lookup NU author(s): Dr Zainal Ahmad, Dr Jie ZhangORCiD


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Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures against air pollution, such as reducing the effect of a predicted air pollution peak on the surrounding population and ecosystem. In this study a single Feed-forward Artificial Neural Network (FANN) is shown to be able to predict the Air Pollution Index (API) with a Mean Squared Error (MSE) and coefficient determination, R2, of 0.1856 and 0.7950 respectively. However, due to the non-robust nature of single FANN, a selective combination of Multiple Neural Networks (MNN) is introduced using backward elimination and a forward selection method. The results show that both selective combination methods can improve the robustness and performance of the API prediction with the MSE and R2 of 0.1614 and 0.8210 respectively. This clearly shows that it is possible to reduce the number of networks combined in MNN for API prediction, without losses of any information in terms of the performance of the final API prediction model.

Publication metadata

Author(s): Ahmad Z, Rahim NA, Bahadori A, Zhang J

Publication type: Article

Publication status: Published

Journal: IIUM Engineering Journal

Year: 2017

Volume: 18

Issue: 1

Pages: 1-12

Online publication date: 30/05/2017

Acceptance date: 27/09/2016

ISSN (print): 1511-788X

ISSN (electronic): 2289-7860

Publisher: International Islamic University Malaysia (IIUM)