Sharma, Ekta and Deo, Ravinesh C. 
ORCID: https://orcid.org/0000-0002-2290-6749 and Prasad, Ramendra and Parisi, Alfio and Raj, Nawin
(2020)
Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks.
IEEE Access, 8.
pp. 209503-209516.
ISSN 2169-3536
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Text (Published Version)
09262844.pdf Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview |
Abstract
Public health risks arising from airborne pollutants, e.g ., Total Suspended Particulate ( TSP ) matter, can significantly elevate ongoing and future healthcare costs. The chaotic behaviour of air pollutants posing major difficulties in tracking their three-dimensional movements over diverse temporal domains is a significant challenge in designing practical air quality systems. This research paper builds a deep learning hybrid CLSTM model where convolutional neural network ( CNN ) is amalgamed with the long short-term memory ( LSTM ) network to forecast hourly TSP . The CNN model entails a data processer including feature extractors that draw upon statistically significant antecedent lagged predictor variables, whereas the LSTM model encapsulates a new feature mapping scheme to predict the next hourly TSP value. The hybrid CLSTM model is comprehensively benchmarked and is seen to outperform an ensemble of five machine learning models. The efficacy of the CLSTM model is elucidated in model testing phase at study sites in Queensland, Australia. Using performance metrics, visual analysis of TSP simulations relative to observations, and detailed error analysis, this study ascertains the CLSTM model’s practical utility for air pollutant forecasting systems in health risk mitigation. This study captures a feasible opportunity to emulate air quality at relatively high temporal resolutions in global regions where air pollution is a considerable threat to public health.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -) |
| Faculty/School / Institute/Centre: | Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -) |
| Date Deposited: | 03 Dec 2020 05:56 |
| Last Modified: | 03 Jan 2021 23:28 |
| Uncontrolled Keywords: | Air quality forecasting, convolutional neural networks, deep learning, long short-term memory networks |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems |
| Fields of Research (2020): | 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation |
| Socio-Economic Objectives (2020): | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing |
| Identification Number or DOI: | https://doi.org/10.1109/ACCESS.2020.3039002 |
| URI: | http://eprints.usq.edu.au/id/eprint/40204 |
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