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

Cabaneros, Sheen Mclean and Calautit, John Kaiser and Hughes, Ben Richard (2019) A review of artificial neural network models for ambient air pollution prediction. Environmental Modelling and Software, 119. pp. 285-304. ISSN 1364-8152

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    Abstract

    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.

    • Item type:Article
      ID code:69357
      Dates:
      Date
      Event
      1 September 2019
      Published
      30 June 2019
      Published Online
      26 June 2019
      Accepted
      Keywords:air pollution, artificial neural networks, backpropagation algorithm, forecasting, multilayer perceptron, Mechanical engineering and machinery, Software, Environmental Engineering, Ecological Modelling
      Subjects:Technology > Mechanical engineering and machinery
      Department:Faculty of Engineering > Mechanical and Aerospace Engineering
      Depositing user: Pure Administrator
      Date deposited:14 Aug 2019 10:44
      Last modified:11 Apr 2021 00:49
      Related URLs:
      URI:https://strathprints.strath.ac.uk/id/eprint/69357
    CORE (COnnecting REpositories)