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Data-driven Air Quality Characterisation for Urban Environments: a Case Study

Zhou, Yuchao, De, Suparna, Ewa, Gideon, Perera, Charith and Moessner, Klaus (2018) Data-driven Air Quality Characterisation for Urban Environments: a Case Study IEEE Access, 6 (1). pp. 77996-78006.

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The economic and social impact of poor air quality in towns and cities is increasingly being recognised, 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 (AQI), 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 (NARX) 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
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Ewa, Gideon
Perera, Charith
Date : 3 December 2018
Funders : Horizon 2020
DOI : 10.1109/ACCESS.2018.2884647
Copyright Disclaimer : © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
Uncontrolled Keywords : Air Quality Estimation; Air Pollution; Machine Learning Prediction; Neural Network
Depositing User : Clive Harris
Date Deposited : 05 Dec 2018 11:42
Last Modified : 15 Jan 2019 16:09

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