Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data

Abstract In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current metho...

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Autori principali:	Raj Anand Sundaramoorthy, Antony Dennis Ananth, Koteeswaran Seerangan, Malarvizhi Nandagopal, Balamurugan Balusamy, Shitharth Selvarajan
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Nature Portfolio 2024-08-01
Serie:	Scientific Reports
Soggetti:	Contamination of air Air Quality Index Ambient air quality prediction Fused Eurasian Oystercatcher-Pathfinder Algorithm Multiscale depth-wise separable adaptive temporal convolutional network
Accesso online:	https://doi.org/10.1038/s41598-024-68793-x
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