A review of artificial neural network models for ambient air pollution prediction - Cabaneros, Sheen Mclean; Calautit, John Kaiser; Hughes, Ben Richard (Environmental modelling & software : with environment data news, 2019)
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. •Research activity in ambient air pollution forecasting with ANNs continues to grow .•Forecasting of outdoor PM10, PM2.5, nitrogen oxides and ozone levels was widely done .•Feedforward and hybrid ANN model types were predominantly used .•Most of the identified model building steps were done in an ad-hoc manner .