Air pollution index prediction using multiple neural networks

Ahmad, Zainal and Rahim, Nazira Aniza and Bahadori, Alireza and Jie, Zhang (2017) Air pollution index prediction using multiple neural networks. International Islamic University Malaysia Engineering Journal, 18 (1). pp. 1-12. ISSN 1511-788X

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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.

Item Type: Article
Subjects: T Technology > TP Chemical Technology > TP155-156 Chemical engineering
Divisions: Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraan Kimia (School of Chemical Engineering) > Article
Zoom Profil Pakar (Expert Profile) > Zainal Ahmad (Chemical Engineering)
Depositing User: Mr Noorazilan Noordin
Date Deposited: 08 Jan 2018 02:07
Last Modified: 08 Jan 2018 02:08

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