Efficient Deep Learning-driven Approach for PM2.5 Forecasting at Different Locations in Spain

Abdelkader Dairi, Fouzi Harrou, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Forecasting dust pollution is necessary for achieving satisfactory air quality. This work proposes an improved deep learning-based forecasting approach for PM2.5 concentration forecasting. Importantly, this approach introduces an improved variational autoencoder (VAE) model by incorporating a bidirectional gated recurrent unit (BiGRU) at the encoder side of the VAE model. The forecasting quality of the coupled model is verified via comparisons with the traditional VAE model when forecasting PM2.5 concentration time-series data. The assessment is carried out using five statistical metrics. PM2.5 datasets from different stations in Spain are used in this study. Results reveal the accuracy of the improved VAE model for PM2.5 concentration forecasting over the traditional VAE, LSTM, GRU, biLSTM, and BiGRU.
Original languageEnglish (US)
Title of host publication2021 IEEE 3rd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)
ISBN (Print)978-1-7281-9305-2
StatePublished - Aug 13 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-11-20
Acknowledged KAUST grant number(s): OSR-2019-CRG7-3800
Acknowledgements: This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.


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