Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks

Ditsuhi Iskandaryan (), Francisco Ramos () and Sergio Trilles ()
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Ditsuhi Iskandaryan: Institute of New Imaging Technologies (INIT), Universitat Jaume I
Francisco Ramos: Institute of New Imaging Technologies (INIT), Universitat Jaume I
Sergio Trilles: Institute of New Imaging Technologies (INIT), Universitat Jaume I

A chapter in Advances and New Trends in Environmental Informatics, 2023, pp 111-128 from Springer

Abstract: Abstract 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 January–June 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.

Keywords: Graph neural network; Attention temporal graph convolutional network; Spatiotemporal prediction; Nitrogen dioxide prediction (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-18311-9_7

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DOI: 10.1007/978-3-031-18311-9_7

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