Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/159451
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https://doi.org/10.1007/978-3-031-18311-9_7 |
Metadatos
Título
Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural NetworksFecha de publicación
2022-11-10Editor
SpringerISBN
978-3-031-18310-2; 978-3-031-18311-9Cita bibliográfica
Iskandaryan, D., Ramos, F., Trilles, S. (2023). Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks. In: Wohlgemuth, V., Naumann, S., Behrens, G., Arndt, HK., Höb, M. (eds) Advances and New Trends in Environmental Informatics. ENVIROINFO 2022. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-031-18311-9_7Tipo de documento
info:eu-repo/semantics/conferenceObjectVersión de la editorial
https://link.springer.com/chapter/10.1007/978-3-031-18311-9_7#chapter-infoVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Air quality prediction, especially spatiotemporal prediction,
is still a challenging issue. Considering the impact of numerous factors on
air quality causes difficulties in integrating these factors in a spatiotemporal ... [+]
Air quality prediction, especially spatiotemporal prediction,
is still a challenging issue. Considering the impact of numerous factors on
air quality causes difficulties in integrating these factors in a spatiotemporal dimension and developing a model to make efficient predictions. At
the same time, machine learning and deep learning development bring
advanced approaches to addressing these challenges and propose novel solutions. The current work introduces one of the most advanced methods,
an attention temporal graph convolutional network, which was implemented on datasets constructed by combining air quality, meteorological
and traffic data on a spatiotemporal axis. The datasets were obtained
from the city of Madrid for the periods January-June 2019 and JanuaryJune 2020. The evaluation metrics, the Root Mean Square Error and the
Mean Absolute Error confirmed the proposed model’s advantages compared with long short-term memory (reference model). Particularly, it
outperformed the latter method by 14.18% and 3.78%, respectively. [-]
Descripción
Ponència presentada en ENVIROINFO 2022: Advances and New Trends in Environmental Informatics pp 111–128
Publicado en
ENVIROINFO 2022: Advances and New Trends in Environmental Informatics pp 111–128Entidad financiadora
Universitat Jaume I | Ministerio de Ciencia, Innovación y Universidades
Código del proyecto o subvención
PINV2018 | PREDOC/2018/61 | IJC2018-035017-I
Derechos de acceso
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
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