Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots

Sharma, Ekta and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Soar, Jeffrey ORCID: https://orcid.org/0000-0002-4964-7556 and Prasad, Ramendra and Parisi, Alfio V. ORCID: https://orcid.org/0000-0001-8430-8907 and Raj, Nawin ORCID: https://orcid.org/0000-0002-8364-2644 (2022) Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots. Atmospheric Environment, 279:119111. pp. 1-13. ISSN 1352-2310


Abstract

More timely and accurate air quality forecasting could contribute to better public health protection and air pollution prevention. Particulates are a significant indicator for measuring the degree of air pollution. This paper reports on research to model an early warning tool for coarse particulates when assessing the impact of the 12 satellite-derived and ground-based meteorological pollutants out of 30 pollutants considered using hourly Australian data from January 2018–December 2020. A one-dimensional convolutional neural network (CNN) was integrated with a one-directional fully gated recurrent unit (GRU) to forecast consecutive hours' air quality. The CNN model acts as a spatial feature extractor, whereas the new generation GRU makes it computationally efficient. The resultant hybrid ‘CNN-GRU’ is then comprehensively benchmarked outperforming an ensemble of six other deep learning models. The proposed model's efficacy is indicated at the four most air polluted Australian postcodes in the testing phase. A detailed error analysis with visual and statistical metrics for air quality forecasting ascertains the proposed model's countermeasure to reduce harm and loss. The practical tool is immensely beneficial and can be widely deployed to the regions of public health concern where air pollution is a significant hazard.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 26 May 2022 03:50
Last Modified: 26 May 2022 03:50
Uncontrolled Keywords: Deep learning; Machine learning; Air quality forecasting; Convolutional neural network; Gated recurrent unit
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
37 EARTH SCIENCES > 3701 Atmospheric sciences > 370102 Air pollution processes and air quality measurement
40 ENGINEERING > 4011 Environmental engineering > 401101 Air pollution modelling and control
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
Identification Number or DOI: https://doi.org/10.1016/j.atmosenv.2022.119111
URI: http://eprints.usq.edu.au/id/eprint/48582

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