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Data-driven air quality characterization for urban environments: a case study

Zhou, Yuchao, De, Suparna, Ewa, Gideon, Perera, Charith ORCID: and Moessner, Klaus 2018. Data-driven air quality characterization for urban environments: a case study. IEEE Access 6 , pp. 77996-78006. 10.1109/ACCESS.2018.2884647

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The economic and social impact of poor air quality in towns and cities is increasingly being recognized, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the air quality index, using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel non-linear autoregressive neural network with exogenous input model, especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning-based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 2169-3536
Date of First Compliant Deposit: 12 August 2020
Date of Acceptance: 21 November 2018
Last Modified: 12 May 2023 17:29

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