Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Novel Deep Learning Approach to Predict Air Quality Index
Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh.
Department of Computer Science and Engineering, Port City International University, Chattogram, Bangladesh.
Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh.ORCID iD: 0000-0002-7473-8185
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2021 (English)In: Proceedings of International Conference on Trends in Computational and Cognitive Engineering: Proceedings of TCCE 2020 / [ed] M. Shamim Kaiser, Anirban Bandyopadhyay, Mufti Mahmud, Kanad Ray, Singapore: Springer, 2021, p. 367-381Conference paper, Published paper (Refereed)
Abstract [en]

In accordance with the World Health Organization’s instruction, the air quality in Bangladesh is considered perilous. A productive and precise air quality index (AQI) is a must and one of the obligatory conditions for helping the society to be viable in lieu of the consequences of air contamination. If we know the index of air quality in advance, then it would be of a great help saving our health from air contamination. This study introduces an air quality index prediction model for two mostly polluted cities in Bangladesh: Dhaka and Chattogram. Gated recurrent unit (GRU), long short-term memory (LSTM) are the two robust variation of recurrent neural network (RNN). This model combines these two together. We have used GRU as first hidden layer and LSTM as the second hidden layer of the model, followed by two dense layers. After collecting and processing the data, the model was trained on 80% of the data and then validated against the remaining data. We have evaluated the performance of the model considering MSE, RMSE, and MAE to see how much error does the model produce. Results reflect that our model can follow the actual AQI trends for both cities. At last, we have juxtaposed the performance of our proposed hybrid model against a standalone GRU model and a standalone LSTM model. Results also show that combining these two models improves the overall model’s performance.

Place, publisher, year, edition, pages
Singapore: Springer, 2021. p. 367-381
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 1309
Keywords [en]
Air quality index, AQI prediction, Time series analysis, Deep learning, Hybrid neural network
National Category
Computer and Information Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-82071DOI: 10.1007/978-981-33-4673-4_29Scopus ID: 2-s2.0-85098283614OAI: oai:DiVA.org:ltu-82071DiVA, id: diva2:1511707
Conference
2nd International Conference on Trends in Computational and Cognitive Engineering (TCCE 2020), 17-18 December, 2020, Dhaka, Bangladesh
Note

ISBN för värdpublikation: 978-981-33-4672-7, 978-981-33-4673-4

Available from: 2020-12-19 Created: 2020-12-19 Last updated: 2025-02-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Hossain, Mohammad ShahadatAndersson, Karl

Search in DiVA

By author/editor
Hossain, Mohammad ShahadatAndersson, Karl
By organisation
Computer Science
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 192 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf