Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting

The use of data-driven techniques such as artificial neural network (ANN) models for outdoor air pollution forecasting has been popular in the past two decades. However, research activity on uncertainty surrounding the development of ANN models has been limited. Therefore, this review outlines the approaches for addressing model uncertainty according to the steps for building ANN models. Based on 128 articles published from 2000 to 2022, the review reveals that input uncertainty was predominantly addressed while less focus was given to the structure, parameter and output uncertainties. Ensemble approaches have been mostly employed, followed by neuro-fuzzy networks. However, the direct measurement of uncertainty received less attention. The use of bootstrapping, Bayesian, and Monte Carlo simulation techniques which can quantify uncertainty was also limited. In conclusion, this review recommends the development and application of approaches that can both handle and quantify uncertainty surrounding the development of ANN models. • A review was on the methods used to address the uncertainty surrounding the development of ANN models for air pollution forecasting was conducted. • Input uncertainty was predominantly addressed. • Ensemble and neuro-fuzzy approaches were popularly employed. • The adoption of methods that can both handle and quantify model uncertainty was limited.