Air pollution index prediction using multiple neural networks

Air pollution index prediction using multiple neural networks. IIUM Engineering Journal, 18 (1). pp. 1-12. ISSN 1511-788X (2017)


Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures again stair 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 R2of 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.

Item Type: Article
Keywords: Air pollution index, Artificial neural networks, Multiple neural networks, Forward selection, Backward elimination
Taxonomy: By Subject > College of Engineering > Chemical Engineering > Environment
Local Content Hub: Subjects > College of Engineering
Depositing User: Eza Eliana Abdul Wahid
Date Deposited: 29 Dec 2022 09:08
Last Modified: 29 Dec 2022 09:08
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