Abstract
In recent years, air pollution forecasting has become an important reference for governments when formulating environmental policies. However, accurate prediction of regional air quality has become a challenge due to the sparse spatial distribution of atmospheric monitoring stations. To address this problem, this paper proposes a neural network cooperative training and prediction model called “ST-GRNN”. The model incorporates complex network, Extreme Learning Machine (ELM), Long Short-Term Memory Network (LSTM), and Generalized Regression Neural Network (GRNN) to identify spatio-temporal features and accurately predict regional air quality. Comparative experiments using real datasets to predict PM2.5 concentrations show that the accuracy of the ST-GRNN model outperforms other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
An, J., Huang, M., Wang, Z., et al.: Numerical regional air quality forecast tests over the Mainland of China. In: Satake, K., et al. (eds.) Acid rain 2000, pp. 1781–1786. Springer, Dordrecht (2001). https://doi.org/10.1007/978-94-007-0810-5_144
Taheri Shahraiyni, H., Sodoudi, S.: Statistical modeling approaches for PM10 prediction in urban areas; a review of 21st-century studies. Atmosphere 7(2), 15 (2016)
Zhu, S., Lian, X., Liu, H., et al.: Daily air quality index forecasting with hybrid models: a case in China. Environ. Pollut. 231, 1232–1244 (2017)
Jaiswal, J.K., Samikannu, R.: Application of random forest algorithm on feature subset selection and classification and regression. In: 2017 World Congress on Computing and Communication Technologies (WCCCT), pp. 65–68. IEEE (2017)
Geng, X., He, X., Xu, L., et al.: Graph correlated attention recurrent neural network for multivariate time series forecasting. Inf. Sci. 606, 126–142 (2022)
Pan, C., Tan, J., Feng, D.: Prediction intervals estimation of solar generation based on gated recurrent unit and kernel density estimation. Neurocomputing 453, 552–562 (2021)
Fang, W., Zhuo, W., Song, Y., et al.: \(\Delta \)free-LSTM: an error distribution free deep learning for short-term traffic flow forecasting. Neurocomputing 526, 180–190 (2023)
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)
Bruna, J., Zaremba, W., Szlam, A., et al.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
Jiang, N., Jie, W., Li, J., et al.: GATrust: a multi-aspect graph attention network model for trust assessment in OSNs. IEEE Trans. Knowl. Data Eng. (2022)
Chen, Z., Deng, Q., Zhao, Z., et al.: Energy consumption prediction of cold source system based on GraphSAGE. IFAC-PapersOnLine 54(11), 37–42 (2021)
Stein, A.F., Draxler, R.R., Rolph, G.D., et al.: NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96(12), 2059–2077 (2015)
Bahiraei, M., Hosseinalipour, S.M.: Thermal dispersion model compared with Euler-Lagrange approach in simulation of convective heat transfer for nanoparticle suspensions. J. Dispers. Sci. Technol. 34(12), 1778–1789 (2013)
Carvalho, J.C., De Vilhena, M.T.M.B.: Pollutant dispersion simulation for low wind speed condition by the ILS method. Atmos. Environ. 39(34), 6282–6288 (2005)
Deng, Z., Weng, D., Chen, J., et al.: AirVis: visual analytics of air pollution propagation. IEEE Trans. Vis. Comput. Graph. 26(1), 800–810 (2019)
Ren, K., Wu, Y., Zhang, H., et al.: Visual analytics of air pollution propagation through dynamic network analysis. IEEE Access 8, 205289–205306 (2020)
Siwek, K., Osowski, S.: Data mining methods for prediction of air pollution. Int. J. Appl. Math. Comput. Sci. 26(2), 467–478 (2016)
Zhou, H., Zhang, F., Du, Z., et al.: A theory-guided graph networks based PM2. 5 forecasting method. Environ. Pollut. 293, 118569 (2022)
Zhao, G., He, H., Huang, Y., et al.: Near-surface PM2. 5 prediction combining the complex network characterization and graph convolution neural network. Neural Comput. Appl. 33, 17081–17101 (2021)
Chen, C., Zhang, Q., Kashani, M.H., et al.: Forecast of rainfall distribution based on fixed sliding window long short-term memory. Eng. Appl. Comput. Fluid Mech. 16(1), 248–261 (2022)
Seinfeld, J.H.: Urban air pollution: state of the science. Science 243(4892), 745–752 (1989)
Qin, Y., Chi, X., Sheng, B., et al.: GuideRender: large-scale scene navigation based on multi-modal view frustum movement prediction. Vis. Comput. 1–11 (2023)
Xie, Z., Zhang, W., Sheng, B., et al.: BaGFN: broad attentive graph fusion network for high-order feature interactions. IEEE Trans. Neural Netw. Learn. Syst. (2021)
An, H., Zheng, L.: RETRACTED ARTICLE: ambient air quality prediction and unbounded variational continuous function based on big data. Arab. J. Geosci. 14(17), 1737 (2021)
Wang, J., Li, J., Wang, X., et al.: An quality prediction model based on CNN-BiNLSTM-attention. Environ. Dev. Sustain. 1–16 (2022)
Acknowledgements
This study was supported by the Natural Science Foundation of Sichuan Province (Grant No. 2022NSFSC0961), the Doctoral Foundation of Southwest University of Science and Technology (Grant No. 19zx7144), and the Special Research Fund of the Research Centre for Network Emergency Management in China (Mianyang) Science and Technology City (Grant No. WLYJGL2023ZD04).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, S., Wang, S., Liu, Y., Ma, D. (2024). The ST-GRNN Cooperative Training Model Based on Complex Network for Air Quality Prediction. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-50075-6_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50074-9
Online ISBN: 978-3-031-50075-6
eBook Packages: Computer ScienceComputer Science (R0)