Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas

Babu Saheer, Lakshmi, Bhasy, Ajay, Maktab Dar Oghaz, Mahdi and Zarrin, Javad (2022) Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas. Frontiers in Big Data, 5. p. 822573. ISSN 2624-909X

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Official URL: https://doi.org/10.3389/fdata.2022.822573

Abstract

Monitoring, predicting and controlling the air quality in urban areas is one of the effective solutions for tackling the climate change problem. Leveraging the availability of big data in different domains like pollutant concentration, urban traffic, aerial imagery of terrains and vegetation, and weather conditions can aid in understanding the interactions between these factors and building a reliable air quality prediction model. %Majority of the existing air-quality models do not include these related factors. This research proposes a novel cost-effective and efficient air quality modeling framework including all these factors employing state-of-the-art artificial intelligence techniques. The framework also includes a novel deep learning-based vegetation detection system using aerial images. The pilot study conducted for the UK city of Cambridge using the proposed framework investigates various predictive models ranging from statistical to machine learning and deep recurrent neural network models. This framework opens up possibilities of broadening air quality modeling and prediction to other domains like vegetation or green space planning or green traffic routing for sustainable urban cities. The research is mainly focused on extracting strong pieces of evidence which could be useful in proposing better policies around climate change.

Item Type: Journal Article
Keywords: urban air quality, climate change mitigation, data mining and analysis, urban vegetation detection, statistical modeling, machine learning and deep learning algorithms, predictive modeling, regression based prediction algorithms, aerial view image recognition, cost effective modeling
Faculty: Faculty of Science & Engineering
Depositing User: Lisa Blanshard
Date Deposited: 03 Mar 2022 11:13
Last Modified: 09 Jun 2022 10:30
URI: https://arro.anglia.ac.uk/id/eprint/707363

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