Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO2 pollution
Cabaneros, Sheen Mclean S. and Calautit, John Kaiser S. and Hughes, Ben Richard (2017) Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO2 pollution. Energy Procedia, 142. pp. 3524-3530. ISSN 1876-6102 (https://doi.org/10.1016/j.egypro.2017.12.240)
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Abstract
Traffic-related air pollution has been a serious concern amongst policy-makers and the public due to its physiological and environmental impacts. An early warning system based on accurate forecasting tools must therefore be implemented to circumvent the adverse effects of exposure to major air pollutants. A multilayer perceptron neural network was trained and developed using air pollution and meteorological data over a two-year period from a monitoring site in Marylebone Road, Central London to predict roadside concentration values of NO2 24 hours ahead. Several hybrid models were also developed by applying feature selection techniques such as stepwise regression, principal component analysis, and Classification and Regression Trees to the neural network model. Most roadside pollutant variables, e.g., oxides of nitrogen, were found to be significant in predicting NO2. The statistical results reveal overall prediction superiority of the hybrid models to the standalone neural network model.
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Item type: Article ID code: 67883 Dates: DateEvent31 December 2017Published30 May 2017AcceptedSubjects: Technology > Mechanical engineering and machinery Department: Faculty of Engineering > Mechanical and Aerospace Engineering Depositing user: Pure Administrator Date deposited: 16 May 2019 11:15 Last modified: 12 Sep 2024 00:51 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/67883