Author ORCID Identifier

https://orcid.org/0000-0002-2248-1701

Document Type

Conference Paper

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences, Environmental sciences

Publication Details

https://2023.ieeeigarss.org/view_paper.php?PaperNum=2198

International Geoscience and Remote Sensing Symposium, IGARSS Pasadena, California, 2033

Abstract

Predicting air pollutant concentrations is an efficient way to prevent incidents by providing early warnings of harmful air pollutants. A precise prediction of air pollutant concentrations is an important factor in controlling and preventing air pollution. In this paper, we develop a bidirectional long-short-term memory and a bidirectional gated recurrent unit (BiLSTM−BiGRU) to predict PM 2.5 concentrations in a target city for different lead times. The BiLSTM extracts preliminary features, and the BiGRU further extracts deep features from air pollutant and meteorological data. The fully connected (FC) layer receives the output and makes an accurate prediction of the PM 2.5 concentration. The model is then compared with five other deep learning models in terms of root mean square error (RMSE), mean absolute error (MAE) and correlation (R 2 ) over different lead times. The results indicate that the proposed model has at least 2.2 times lower RMSE than the other models.

DOI

https://doi.org/10.21427/w3n9-ac56

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

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