Babu-Saheer_et_al_2022.pdf (3.59 MB)
Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas
journal contribution
posted on 2023-07-26, 15:42 authored by Lakshmi Babu Saheer, Ajay Bhasy, Mahdi Maktab Dar Oghaz, Javad ZarrinMonitoring, 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.
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Refereed
- Yes
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5Page range
822573Publication title
Frontiers in Big DataISSN
2624-909XExternal DOI
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Frontiers MediaFile version
- Published version
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- eng
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Legacy posted date
2022-03-03Legacy creation date
2022-03-25Legacy Faculty/School/Department
Faculty of Science & EngineeringUsage metrics
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