Efficient Deep Learning-driven Approach for PM2.5 Forecasting at Different Locations in Spain
KAUST DepartmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Environmental Statistics Group
KAUST Grant NumberOSR-2019-CRG7-3800
Online Publication Date2021-08-13
Print Publication Date2021-05-28
Permanent link to this recordhttp://hdl.handle.net/10754/670626
MetadataShow full item record
AbstractForecasting 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.
CitationDairi, A., Harrou, F., & Sun, Y. (2021). Efficient Deep Learning-driven Approach for PM2.5 Forecasting at Different Locations in Spain. 2021 IEEE 3rd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). doi:10.1109/ecbios51820.2021.9510462
SponsorsThis 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.
Conference/Event name2021 IEEE 3rd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)