The air quality predication based on machine learning methods

Research Article
Open access

The air quality predication based on machine learning methods

Maohao Ran 1*
  • 1 Arizona State University    
  • *corresponding author mran2@asu.edu
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230715
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Air quality is the focus of attention all over the world. As an important index to measure air quality, accurate prediction of PM2.5 plays an essential role in regulating air quality. This paper uses different machine learning algorithms and neural networks to infer air quality (AQ) in the study area and observe the impact of these methods on accuracy. The results indicate that shuffling the data can enhance the model's performance. In addition, the neural network is the most affected by the data shuffle operation compared to other models. In the case of a shuffle operation, the performance of the neural networks is the lowest among all models. However, in the case of non-shuffle data, the neural network performance is the best among all models. Therefore, in the absence of large-scale data sets, the traditional machine learning method with a relatively small scale should be selected to model the air quality prediction problem because the traditional machine learning method performs better in the small sample data scene.

Keywords:

neural networks, shuffle data, environment predication.

Ran,M. (2023). The air quality predication based on machine learning methods. Applied and Computational Engineering,6,1296-1302.
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References

[1]. Zhang, J., & Ding, W. (2017). Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong. International journal of environmental research and public health, 14(2), 114.

[2]. Zhou, X., Xu, J., Zeng, P., & Meng, X. (2019, February). Air pollutant concentration prediction based on GRU method. In Journal of Physics: Conference Series (Vol. 1168, No. 3, p. 032058).IOP Publishing.

[3]. Huang, C. J., & Kuo, P. H. (2018). A deep CNN-LSTM model for particulate matter (PM2. 5) forecasting in smart cities. Sensors, 18(7), 2220.

[4]. Soh, P. W., Chang, J. W., & Huang, J. W. (2018). Adaptive deep learning-based air quality prediction model using the most relevant spatial-temporal relations. Ieee Access, 6, 38186-38199.

[5]. Pak, U., Ma, J., Ryu, U., Ryom, K., Juhyok, U., Pak, K., & Pak, C. (2020). Deep learning-based PM2. 5 prediction considering the spatiotemporal correlations: A case study of Beijing, China. Science of The Total Environment, 699, 133561.

[6]. Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., ... & Zhang, Q. (2020). Spectral temporal graph neural network for multivariate time-series forecasting. Advances in neural information processing systems, 33, 17766-17778.

[7]. Meng, Q., Ke, G., Wang, T., Chen, W., Ye, Q., Ma, Z. M., & Liu, T. Y. (2016). A communication-efficient parallel algorithm for decision tree. Advances in Neural Information Processing Systems, 29.

[8]. Klein, A., Falkner, S., Bartels, S., Hennig, P., & Hutter, F. (2017, April). Fast bayesian optimization of machine learning hyperparameters on large datasets. In Artificial intelligence and statistics (pp. 528-536). PMLR.

[9]. Hamid, A. J., & Ahmed, T. M. (2016). Developing prediction model of loan risk in banks using data mining. Machine Learning and Applications: An International Journal (MLAIJ), 3(1), 1-9.

[10]. Gross, J., & Groß, J. (2003). Linear regression (Vol. 175). Springer Science & Business Media.

Cite this article

Ran,M. (2023). The air quality predication based on machine learning methods. Applied and Computational Engineering,6,1296-1302.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Zhang, J., & Ding, W. (2017). Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong. International journal of environmental research and public health, 14(2), 114.

[2]. Zhou, X., Xu, J., Zeng, P., & Meng, X. (2019, February). Air pollutant concentration prediction based on GRU method. In Journal of Physics: Conference Series (Vol. 1168, No. 3, p. 032058).IOP Publishing.

[3]. Huang, C. J., & Kuo, P. H. (2018). A deep CNN-LSTM model for particulate matter (PM2. 5) forecasting in smart cities. Sensors, 18(7), 2220.

[4]. Soh, P. W., Chang, J. W., & Huang, J. W. (2018). Adaptive deep learning-based air quality prediction model using the most relevant spatial-temporal relations. Ieee Access, 6, 38186-38199.

[5]. Pak, U., Ma, J., Ryu, U., Ryom, K., Juhyok, U., Pak, K., & Pak, C. (2020). Deep learning-based PM2. 5 prediction considering the spatiotemporal correlations: A case study of Beijing, China. Science of The Total Environment, 699, 133561.

[6]. Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., ... & Zhang, Q. (2020). Spectral temporal graph neural network for multivariate time-series forecasting. Advances in neural information processing systems, 33, 17766-17778.

[7]. Meng, Q., Ke, G., Wang, T., Chen, W., Ye, Q., Ma, Z. M., & Liu, T. Y. (2016). A communication-efficient parallel algorithm for decision tree. Advances in Neural Information Processing Systems, 29.

[8]. Klein, A., Falkner, S., Bartels, S., Hennig, P., & Hutter, F. (2017, April). Fast bayesian optimization of machine learning hyperparameters on large datasets. In Artificial intelligence and statistics (pp. 528-536). PMLR.

[9]. Hamid, A. J., & Ahmed, T. M. (2016). Developing prediction model of loan risk in banks using data mining. Machine Learning and Applications: An International Journal (MLAIJ), 3(1), 1-9.

[10]. Gross, J., & Groß, J. (2003). Linear regression (Vol. 175). Springer Science & Business Media.

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