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Single Pollutant Prediction Approach by Fusing MLSTM and CNN

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 13370)


Air pollution has a negative impact on people’s health, and accurate prediction of future air pollutant concentrations is crucial for cities and individuals to take early warning and preventive measures against potential air pollution. In this paper, we propose an air pollutant prediction model, named CMLSTM, that well combines Mogrifier LSTM and CNN to predict a single pollutant for the next six hours using multi-site air pollutant data, meteorological data, and holiday information. Mogrifier LSTM can capture long-term air pollutant time-series features with richer contextual interactions, while CNN uses one-dimensional convolution to effectively model the spatial transport of air pollutants. We conduct experiments with four years of data from one city, and the results demonstrate CMLSTM has higher prediction accuracy than the baseline methods.


  • Air pollutant forecast
  • Mogrifier LSTM
  • CNN
  • Spatio-temporal data mining

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  • DOI: 10.1007/978-3-031-10989-8_11
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  1. Verma, I., Ahuja, R., Meisheri, H., Dey, L.: Air pollutant severity prediction using Bi-directional LSTM Network. In: 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 651–654. IEEE (2018)

    Google Scholar 

  2. Krishan, M., Jha, S., Das, J., et al.: Air quality modelling using long short-term memory (LSTM) over NCT-Delhi. India. Air Qual. Atmos. Health 12(8), 899–908 (2019)

    CrossRef  Google Scholar 

  3. Wang, J., Li, J., Wang, X., Wang, J., Huang, M.: Air quality prediction using CT-LSTM. Neural Comput. Appl. 33(10), 4779–4792 (2020).

    CrossRef  Google Scholar 

  4. Zhao, J., Deng, F., Cai, Y., Chen, J.: Long short-term memory-Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction. Chemosphere 220, 486–492 (2019)

    Google Scholar 

  5. Wen, C., et al.: A novel spatiotemporal convolutional long short-term neural network for air pollution prediction. Sci. Total Environ. 654, 1091–1099 (2019)

    CrossRef  Google Scholar 

  6. Melis, G., Ko čiský, T., Blunsom, P.: Mogrifier LSTM. In: International Conference on Learning Representations, pp. 1–13 (2020)

    Google Scholar 

  7. Binkowski, F.S., Roselle, S.J.: Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description. J. Geophys. Res. Atmos. 108(D6) (2003)

    Google Scholar 

  8. Kumar, U., Jain, V.K.: ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO). Stoch. Environ. Res. Risk Assess. 24(5), 751–760 (2010)

    CrossRef  Google Scholar 

  9. Sánchez, A.S., Nieto, P.G., Fernández, P.R., del Coz Díaz, J.J., Iglesias-Rodríguez, F.J.: Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Math. Comput. Model. 54(5–6), 1453–1466 (2011)

    CrossRef  Google Scholar 

  10. Yu, R., Yang, Y., Yang, L., Han, G., Move, O.A.: RAQ-A random forest approach for predicting air quality in urban sensing systems. Sensors 16(1), 86 (2016)

    CrossRef  Google Scholar 

  11. Xie, H., Ma, F., Bai, Q.: Prediction of indoor air quality using artificial neural networks. In: 2009 Fifth International Conference on Natural Computation, pp. 414–418 (2009)

    Google Scholar 

  12. Cui, R., Liu, M.: RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Comput. Med. Imaging Graph. 73, 1–10 (2019)

    CrossRef  Google Scholar 

  13. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    CrossRef  Google Scholar 

  14. Chen, K., Zhou, Y., Dai, F.: A LSTM-based method for stock returns prediction: a case study of China stock market. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2823–2824. IEEE (2015)

    Google Scholar 

  15. Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., Cottrell, G.: A dual-stage attention-based recurrent neural network for time series prediction. arXiv preprint arXiv:1704.02971 (2017)

  16. Chang, Y.S., Chiao, H.T., Abimannan, S., Huang, Y.P., Tsai, Y.T., Lin, K.M.: An LSTM-based aggregated model for air pollution forecasting. Atmos. Pollut. Res. 11(8), 1451–1463 (2020)

    CrossRef  Google Scholar 

  17. Cheng, W., Shen, Y., Zhu, Y., Huang, L.: A neural attention model for urban air quality inference: learning the weights of monitoring stations. In: Thirty-Second AAAI Conference on Artificial Intelligence, pp. 2151–2158 (2018)

    Google Scholar 

  18. Qiu, H., Zheng, Q., Msahli, M., Memmi, G., Qiu M., Lu, J.: Topological graph convolutional network-based urban traffic flow and density prediction. In: IEEE Transactions on Intelligent Transportation Systems, pp. 4560–4569 (2021)

    Google Scholar 

  19. Han, Y., Zhang, Q., Li, V.O., Lam, J.C.: Deep-AIR: a hybrid CNN-LSTM framework for air quality modeling in metropolitan cities. arXiv preprint arXiv:2103.14587 (2021)

  20. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1724–1734 (2014)

    Google Scholar 

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This work was supported in part by the Inner Mongolia Science and Technology Plan Project (No. 2020GG0187), and Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software, Inner Mongolia Key Laboratory of Social Computing and Data Processing.

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Correspondence to Jing Liu .

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Lian, M., Liu, J. (2022). Single Pollutant Prediction Approach by Fusing MLSTM and CNN. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham.

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  • Print ISBN: 978-3-031-10988-1

  • Online ISBN: 978-3-031-10989-8

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