Graph Neural Network for Air Quality Prediction: A Case Study in Madrid
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Title
Graph Neural Network for Air Quality Prediction: A Case Study in MadridDate
2023Publisher
IEEEBibliographic citation
ISKANDARYAN, Ditsuhi; RAMOS, Francisco; TRILLES, Sergio. Graph Neural Network for Air Quality Prediction: A Case Study in Madrid. IEEE Access, 2023, vol. 11, p. 2729-2742.Type
info:eu-repo/semantics/articlePublisher version
https://ieeexplore.ieee.org/abstract/document/10005808Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
Air quality monitoring, modelling and forecasting are considered pressing and challenging
topics for citizens and decision-makers, including the government. The tools used to achieve the above goals
vary depending ... [+]
Air quality monitoring, modelling and forecasting are considered pressing and challenging
topics for citizens and decision-makers, including the government. The tools used to achieve the above goals
vary depending on the opportunities provided by technological development. Much attention is currently
being paid to machine learning and deep learning methods, which, compared to domain knowledge methods,
often perform better in terms of capturing, computing and processing multidimensional information and
complex dependencies. The technique introduced in this work is an Attention Temporal Graph Convolutional
Network based on a combination of Attention, a Gated Recurrent Unit and a Graph Convolutional Network.
In the framework of the current study, it is initially suggested to use the presented approach in the domain
of air quality prediction. The proposed method was tested using air quality, meteorological and traffic
data obtained from the city of Madrid for the periods January-June 2019 and January-June 2022. The
evaluation metrics, including Root Mean Square Error, Mean Absolute Error and Pearson Correlation
Coefficient, confirmed the proposed model’s advantages compared with the reference models (Temporal
Graph Convolutional Network, Long Short-Term Memory and Gated Recurrent Unit). [-]
Is part of
IEEE Access, 2023, vol. 11, p. 2729-2742Funder Name
Universitat Jaume I | Ministerio de Ciencia e Innovación
Project code
PREDOC/2018/61 | IJC2018-035017-I
Rights
info:eu-repo/semantics/openAccess
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