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

Cabaneros, Sheen Mclean; Hughes, Ben

Abstract

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 the 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 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.

Citation

Cabaneros, S. M., & Hughes, B. (2022). Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting. Environmental modelling & software : with environment data news, 158, Article 105529. https://doi.org/10.1016/j.envsoft.2022.105529

• Journal Article Type: Review
• Acceptance Date: Sep 13, 2022
• Online Publication Date: Sep 24, 2022
• Publication Date: 2022-12
• Deposit Date: Nov 23, 2022
• Publicly Available Date: Sep 25, 2023
• Journal: Environmental Modelling & Software
• Print ISSN: 1364-8152
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 158
• Article Number: 105529
• DOI: https://doi.org/10.1016/j.envsoft.2022.105529
• Keywords: Ecological Modeling; Environmental Engineering; Software
• Public URL: https://hull-repository.worktribe.com/output/4094920