Abstract
People have been paying more and more attention to their surroundings in recent years, and the problem of air quality, which affects a significant portion of our surroundings, warrants our attention. Ground-level ozone, particulate pollutants, carbon monoxide, sulfur dioxide, and nitrogen dioxide make up the five components of the Air Quality Index, which is a key index for describing the state of the air. Predicting Air Quality Index values using deep learning is crucial for addressing the issue of air pollution. This study compares the prediction accuracy of Long Short-Term Memory and Convolutional Neural Networks on time-series class data, and the results show that Long Short-Term Memory is significantly better than Convolutional Neural Networks in predicting Air Quality Index values. The study uses the actual Air Quality Index data of a specific city for 720 consecutive hours to predict Air Quality Index values using deep learning. When creating the model based on the visualization, a visual presentation is created to demonstrate the data, prediction model, and metrics for calculating the Air Quality Index. The visual display and model building of Air Quality Index indexes are beneficial to giving us a deeper understanding of air pollution problems, and play an important role in setting further policies to improve air quality and prevent respiratory diseases, etc.
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References
Mi, Y.-Q., Wang, S.-S., Yu, H.-H., Zhang, J.-P., Wang, X., Luo, S., Li, W.: A cross-sectional study on the relationship between cardiovascular and cerebrovascular disease mortality and air quality. Chin. General Med. 21(29), 3545–3550 (2018)
Dockery, D.W., Pope, C.A.: An association between air pollution and mortality in six US cities. New Engl. J. Med. 329(24), 1753–1759 (1993)
Brook, R.D., Rajagopalan, S., Pope, C.A., Brook, J.R., Kaufman, J.D.: Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American heart association. Circulation 121(21), 2331–2378 (2010)
Liu, Y.-Y., Liu, Y.-S., Zheng, J.-W., Chai, F.-X., Li, M., Mu, J.: Research on urban waterlogging prediction method based on BP neural network and numerical model. J. Water Conservancy 53(3), 12 (2022)
Zecchin, C., Facchinetti, A., Sparacino, G., De Nicolao, G., Cobelli, C.: A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information. In: Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, pp. 5653–5656 (2011)
Wang, Y., Wang, L., Yang, F., Di, W., Chang, Q.: Advantages of direct input-to-output connections in neural networks: the Elman network for stock index forecasting. Inf. Sci. 547, 1066–1079 (2021)
Shi, J.-Q.: Application of portable gas chromatography-mass spectrometry in the detection of ambient air pollution incidents. Chem. Eng. 34(8), 3 (2020)
China Science and Technology Network: U.S. creates 2.1 gram “jellyfish flying machine” to detect air pollution. Sci. Technol. Inf. 3, I0020–I0020 (2014)
Zhang, Y.-W., Hu, J.-Y., Wang, R.: Pm2.5 prediction model based on neural network. J. Jiangsu Normal Univ. Nat. Sci. Ed. 33(1), 3 (2015)
Wang, M., Zhou, B., Guo, Y., He, J.-Q.: Spatial prediction of urban pm2.5 concentration based on BP artificial neural network. Environ. Pollut. Prevent. 35(9), 5 (2013)
Zhang, Y.-Y.: Data visualization based on Pyecharts. Comput. Knowl. Technol. Acad. Edn. 18(2), 4 (2022)
Barrett, P., Hunter, J., Miller, J.T., Hsu, J.C., Greenfield, P.: matplotlib—a portable python plotting package. Astron. Data Anal. Softw. Syst. XIV (2005)
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zhang, L., Fang, Z., Ma, T.-F.: Research on AQI prediction by BP neural network with time series wavelet decomposition. J. Xuzhou Eng. College Nat. Sci. Edn. 35(1), 8 (2020)
Kamnitsas, K., Ledig, C., Newcombe, V.F.J., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61 (2016)
Acknowledgment
This research was partly supported by 61901206 National Natural Science Foundation of China, and partly supported by 2019JZZY010134 Shandong Provincial Key Research and Development Program (Major Science and Technological Innovation Project).
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Ding, L., Sun, J., Shen, T., Jing, C. (2023). A Noval Air Quality Index Prediction Scheme Based on Long Short-Term Memory Technology. In: Xiong, N., Li, M., Li, K., Xiao, Z., Liao, L., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-031-20738-9_6
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DOI: https://doi.org/10.1007/978-3-031-20738-9_6
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