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Managing large dataset gaps in urban air quality prediction: DCU-Insight-AQ at MediaEval 2022

Cuong, Dinh Viet, Le-Khac, Phúc H. orcid logoORCID: 0000-0002-0504-5844, Stapleton, Adam orcid logoORCID: 0000-0003-1233-211X, Eichlemann, Elke, Roantree, Mark orcid logoORCID: 0000-0002-1329-2570 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2023) Managing large dataset gaps in urban air quality prediction: DCU-Insight-AQ at MediaEval 2022. In: MediaEval’22: Multimedia Evaluation Workshop, 13–15 Jan 2023, Bergen, Norway and Online.

Abstract
Calculating an Air Quality Index (AQI) typically uses data streams from air quality sensors deployed at fixed locations and the calculation is a real time process. If one or a number of sensors are broken or offline, then the real time AQI value cannot be computed. Estimating AQI values for some point in the future is a predictive process and uses historical AQI values to train and build models. In this work we focus on gap filling in air quality data where the task is to predict the AQI at 1, 5 and 7 days into the future. The scenario is where one or a number of air, weather and traffic sensors are offline and explores prediction accuracy under such situations. The work is part of the MediaEval’2022 Urban Air: Urban Life and Air Pollution task submitted by the DCU-Insight-AQ team and uses multimodal and crossmodal data consisting of AQI, weather and CCTV traffic images for air pollution prediction.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Science and Health > School of Biotechnology
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of MediaEval 2022. .
Official URL:https://2022.multimediaeval.com/paper9397.pdf
Copyright Information:© 2022 The Authors.
Funders:Science Foundation Ireland (SFI/12/RC/2289_P2), Centre for Research Training in Machine Learning (18/CRT/6183)
ID Code:27961
Deposited On:13 Jan 2023 15:22 by Alan Smeaton . Last Modified 16 Nov 2023 16:30
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