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Air quality prediction using CT-LSTM

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
  • Published:
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Abstract

With the development of industry, air pollution has become a serious problem. It is very important to create an air quality prediction model with high accuracy and good performance. Therefore, a new method of CT-LSTM is proposed in this paper, in which the prediction model is established by combining chi-square test (CT) and long short-term memory (LSTM) network model. CT is used to determine the influencing factors of air quality. The hourly air quality data and meteorological data from Jan. 1, 2017 to Dec. 31, 2018 are used to train the LSTM network model. The data from Jan. 1, 2019 to Dec. 31, 2019 are used to evaluate the LSTM network model. The AQI level of Shijiazhuang of Hebei Province of China from Jan. 1, 2019 to Dec. 31, 2019 is predicted with five methods (SVR, MLP, BP neural network, Simple RNN and this paper's new method). Then, a contrastive analysis of the five prediction results is made. The experimental results show that the accuracy of this new method reaches 93.7%, which is the highest in the five methods and the maximum error of this new method is 1. The correct number of days predicted by this new method is also the highest among the five methods, which is 342 days. The new method also shows good characteristics in MAE, MSE and RMSE, which makes it more accurate for people to predict the AQI level.

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Funding

This work was funded by Natural Science Foundation of Hebei Province, Grant ZD2018236, and Foundation of Hebei University of Science and Technology, Grant 2019-ZDB02.

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Authors and Affiliations

  1. School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China

    Jingyang Wang, Jiazheng Li, Xiaoxiao Wang & Min Huang

  2. College of Computer Science and Engineering, Northeastern University, Shenyang, 110000, China

    Jue Wang

Authors
  1. Jingyang Wang
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  2. Jiazheng Li
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  3. Xiaoxiao Wang
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  4. Jue Wang
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  5. Min Huang
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Correspondence to Min Huang.

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Cite this article

Wang, J., Li, J., Wang, X. et al. Air quality prediction using CT-LSTM. Neural Comput & Applic 33, 4779–4792 (2021). https://doi.org/10.1007/s00521-020-05535-w

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  • DOI: https://doi.org/10.1007/s00521-020-05535-w

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