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Title	A Hybrid Long Short-Term Memory Network Based On Wind And Rain Sensitive And Its Application To Pm2.5 Prediction
ID_Doc	2182
Authors	Huang B.-X.; Lee S.-J.
Year	2023
Published	Lecture Notes in Networks and Systems, 766
DOI	http://dx.doi.org/10.1007/978-3-031-41630-9_23
Abstract	With the rise of health consciousness in recent years, air pollution has become an issue of public concern. The Air Quality Index (AQI), which takes into account various air pollution factors has become an indispensable part of daily life. Of these factors, PM2.5 has a particularly serious impact on the human body. The final Wind-and-Rainfall sensitive LSTM (WRLSTM) can effectively predict PM2.5 concentrations in the next 6–18 h. The results are better than those of common deep learning algorithms, such as MLP regression. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Air Pollution Forecasting; Attention Mechanism; Deep Learning; Smart City

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