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.

在过去的二十年里，使用人工神经网络 (ANN) 模型等数据驱动技术进行室外空气污染预测已经很流行。然而，围绕人工神经网络模型发展的不确定性的研究活动受到限制。因此，本综述概述了根据构建 ANN 模型的步骤解决模型不确定性的方法。根据 2000 年至 2022 年发表的 128 篇文章，该评论显示主要解决了输入不确定性，而较少关注结构、参数和输出不确定性。主要采用集成方法，其次是神经模糊网络。然而，不确定性的直接测量受到的关注较少。使用自举、贝叶斯、可以量化不确定性的蒙特卡罗模拟技术也受到限制。总之，本次审查建议开发和应用可以处理和量化围绕人工神经网络模型开发的不确定性的方法。