Graph Neural Network for Air Quality Prediction: A Case Study in Madrid
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Otros documentos de la autoría: Iskandaryan, Ditsuhi; Ramos Romero, José Francisco; Trilles, Sergio
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Graph Neural Network for Air Quality Prediction: A Case Study in MadridFecha de publicación
2023Editor
IEEECita bibliográfica
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.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/abstract/document/10005808Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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). [-]
Publicado en
IEEE Access, 2023, vol. 11, p. 2729-2742Entidad financiadora
Universitat Jaume I | Ministerio de Ciencia e Innovación
Código del proyecto o subvención
PREDOC/2018/61 | IJC2018-035017-I
Derechos de acceso
info:eu-repo/semantics/openAccess
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