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A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance
Journal of Cleaner Production ( IF 11.1 ) Pub Date : 2021-09-17 , DOI: 10.1016/j.jclepro.2021.129072
Adil Masood 1 , Kafeel Ahmad 1
Affiliation  

Accurate air quality forecasting is critical for systematic pollution control as well as public health and wellness. Most of the traditional forecasting techniques have shown inconsistent predictive accuracy due to the non-linear, dynamic and complex nature of air pollutants. In the past few years, artificial intelligence (AI)-based methods have become the most powerful and forward-looking approaches for air pollution forecasting because of their specific features such as organic learning, high precision, superior generalization, strong fault tolerance, and ease of working with high-dimensional data. This study presents a comprehensive overview of the most widely used AI-based techniques for air pollution forecasting namely Artificial Neural Networks (ANN), Deep Neural Network (DNN), Support vector machine (SVM) and Fuzzy logic through a systematic literature review (SLR). In total 90 papers were selected which were distributed between 2003 and 2021. The SLR aims to classify the literature on AI-based air pollution forecasting from various perspectives, such as input parameters, relative frequency of application of AI techniques, performance, year of publication, journal and geographic distribution and also addresses the corresponding research questions related to this domain. The results showed that the number of citations and publications have been increasing in recent years. The most frequently applied input parameter is the air quality and the best performing AI-based technique is the DNN. On the other hand, Fuzzy logic, DNN and SVM are the three commonly used AI-based techniques for air pollution forecasting. In addition, some technological gaps in the literature and the pros and cons associated with the different AI techniques, were identified and discussed. This review article shows that AI-based techniques have triggered a resurgence of interest in air pollution forecasting and offer great potential to fundamentally change the way air pollution is forecasted in the near future.



中文翻译:

用于空气污染预测的新兴人工智能 (AI) 技术综述:基础、应用和性能

准确的空气质量预测对于系统的污染控制以及公共卫生和福祉至关重要。由于空气污染物的非线性、动态和复杂性,大多数传统预测技术都显示出不一致的预测准确性。近年来,基于人工智能(AI)的方法因其有机学习、高精度、泛化性强、容错性强、易于操作等特点,成为空气污染预测中最强大、最具前瞻性的方法。处理高维数据。本研究全面概述了最广泛使用的基于人工智能的空气污染预测技术,即人工神经网络 (ANN)、深度神经网络 (DNN)、通过系统文献综述 (SLR) 支持向量机 (SVM) 和模糊逻辑。共选出 90 篇论文,分布于 2003 年至 2021 年。 SLR 旨在从输入参数、AI 技术应用的相对频率、性能、出版年份等多个角度对基于 AI 的空气污染预测的文献进行分类、期刊和地理分布,并解决与该领域相关的相应研究问题。结果表明,近年来引用和发表的数量一直在增加。最常用的输入参数是空气质量,性能最佳的基于 AI 的技术是 DNN。另一方面,模糊逻辑、DNN 和 SVM 是三种常用的基于人工智能的空气污染预测技术。此外,确定并讨论了文献中的一些技术差距以及与不同 AI 技术相关的利弊。这篇评论文章表明,基于人工智能的技术引发了人们对空气污染预测的重新关注,并提供了在不久的将来从根本上改变空气污染预测方式的巨大潜力。

更新日期:2021-09-21
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