A review of artificial neural network models for ambient air pollution prediction

Sheen Mclean Cabaneros, John Kaiser Calautit, Ben Richard Hughes

Research output: Contribution to journalReview articlepeer-review

196 Citations (Scopus)
539 Downloads (Pure)


Research activity in the field of air pollution forecasting using artificial neural networks (ANNs) has increased dramatically in recent years. However, the development of ANN models entails levels of uncertainty given the black-box nature of ANNs. In this paper, a protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models. The majority of the reviewed works are aimed at the long-term forecasting of outdoor PM10, PM2.5, and oxides of nitrogen, and ozone. The vast majority of the identified works utilised meteorological and source emissions predictors almost exclusively. Furthermore, ad-hoc approaches are found to be predominantly used for determining optimal model predictors, appropriate data subsets and the optimal model structure. Multilayer perceptron and ensemble-type models are predominantly implemented. Overall, the findings highlight the need for developing systematic protocols for developing powerful ANN models.

Original languageEnglish
Pages (from-to)285-304
Number of pages20
JournalEnvironmental Modelling and Software
Early online date30 Jun 2019
Publication statusPublished - 1 Sept 2019


  • air pollution
  • artificial neural networks
  • backpropagation algorithm
  • forecasting
  • multilayer perceptron


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