Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO2 pollution

Sheen Mclean S. Cabaneros, John Kaiser S. Calautit, Ben Richard Hughes

Research output: Contribution to journalConference articlepeer-review

20 Citations (Scopus)
91 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)3524-3530
Number of pages7
JournalEnergy Procedia
Publication statusPublished - 31 Dec 2017
Event9th International Conference on Applied Energy, ICAE 2017 - Cardiff, United Kingdom
Duration: 21 Aug 201724 Aug 2017


  • air pollution forecasting
  • artificial intelligence
  • artificial neural network
  • multilayer perceptron
  • NO

Fingerprint Dive into the research topics of 'Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO<sub>2</sub> pollution'. Together they form a unique fingerprint.

Cite this