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

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 w...

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Vydáno v:	Frontiers in big data Ročník 5; s. 822573
Hlavní autoři:	Babu Saheer, Lakshmi, Bhasy, Ajay, Maktabdar, Mahdi, Zarrin, Javad
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Media S.A 25.03.2022
Témata:	aerial view image recognition Big Data climate change mitigation machine learning and deep learning algorithms regression based prediction algorithms urban air quality urban vegetation detection urban vegetation detection climate change mitigation machine learning and deep learning algorithms aerial view image recognition regression based prediction algorithms urban air quality cost effective modeling
ISSN:	2624-909X, 2624-909X
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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Shrnutí:	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. 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 in 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Yves Philippe Rybarczyk, Dalarna University, Sweden This article was submitted to Data-driven Climate Sciences, a section of the journal Frontiers in Big Data Reviewed by: Roberto Corizzo, American University, United States; Rasa Zalakeviciute, University of the Americas, Ecuador
ISSN:	2624-909X 2624-909X
DOI:	10.3389/fdata.2022.822573