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A Novel Deep Learning Approach to Predict Air Quality Index

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Proceedings of International Conference on Trends in Computational and Cognitive Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1309))

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh

    Emam Hossain & Mohammad Shahadat Hossain

  2. Department of Computer Science and Engineering, Port City International University, Chattogram, Bangladesh

    Mohd Arafath Uddin Shariff

  3. Pervasive and Mobile Computing Laboratory, Luleå University of Technology, 931 87, Skellefteå, Sweden

    Karl Andersson

Authors
  1. Emam Hossain
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  2. Mohd Arafath Uddin Shariff
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  3. Mohammad Shahadat Hossain
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  4. Karl Andersson
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Corresponding author

Correspondence to Emam Hossain .

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Editors and Affiliations

  1. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  2. National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  3. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  4. Amity University, Jaipur, Rajasthan, India

    Kanad Ray

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Hossain, E., Shariff, M.A.U., Hossain, M.S., Andersson, K. (2021). A Novel Deep Learning Approach to Predict Air Quality Index. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_29

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