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All issues Volume 625 (2025) E3S Web Conf., 625 (2025) 03017 References
Open Access
Issue
E3S Web Conf.
Volume 625, 2025
5th International Conference on Environment Resources and Energy Engineering (ICEREE 2025)
Article Number 03017
Number of page(s) 10
Section Resource Management and Ecosystem Regulation
DOI https://doi.org/10.1051/e3sconf/202562503017
Published online 17 April 2025
  1. Song, M., Wang, S., Yu, H., Yang, L., & Wu, J. (2011). To reduce energy consumption and to maintain rapid economic growth: Analysis of the condition in China based on expended IPAT model. Renewable and Sustainable Energy Reviews, 15(9), 5129-5134. DOI: 10.1016/j.rser.2011.07.043. [CrossRef] [Google Scholar]
  2. Meng, X. (2012). On the harm of environmental pollution to human body. Scientific and Technological Innovation, 04, 18. [Google Scholar]
  3. Liu, Y., Zhang, Y. P., Zhu, C., & Liu, Q. M. (2017). Prediction and monitoring of air quality based on big data and Internet of Things. Journal on Communications, S2, 129-138. [Google Scholar]
  4. Wu, Q., & Lin, H. (2019). A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors. Science of the Total Environment, 683, 808-821. DOI: 10.1016/j.scitotenv.2019.05.288. [CrossRef] [Google Scholar]
  5. Udristioiu, M. T., Mghouchi, Y. E., & Yildizhan, H. (2023). Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning. Journal of Cleaner Production, 421, 138496. DOI: 10.1016/j.jclepro.2023.138496. [CrossRef] [Google Scholar]
  6. Yuan, F. (2019). Analysis and prediction of Xi 'an Air Quality Index based on ARIMA model. Computer Knowledge and Technology, 35, 262-263+270. DOI: 10.14004/j.cnki.ckt.20191220.003. [Google Scholar]
  7. Zhao, R., Gu, X., Xue, B., Zhang, J., & Ren, W. (2018). Short period PM2. 5 prediction based on multivariate linear regression model. PloS one, 13(7), e0201011. DOI: 10.1371/journal.pone.0201011. [CrossRef] [Google Scholar]
  8. Shishegaran, A., Saeedi, M., Kumar, A., & Ghiasinejad, H. (2020). Prediction of air quality in Tehran by developing the nonlinear ensemble model. Journal of Cleaner Production, 259, 120825. DOI: 10.1016/j.jclepro.2020.120825. [CrossRef] [Google Scholar]
  9. Yang, W., Tang, G., Hao, Y., & Wang, J. (2021). A novel framework for forecasting, evaluation and early-warning for the influence of PM10 on public health. Atmosphere, 12(8), 1020. DOI: 10.3390/atmos12081020. [CrossRef] [Google Scholar]
  10. Dun, M., Xu, Z., Chen, Y., & Wu, L. (2020). Short‐ Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine. Mathematical problems in engineering, 2020(1), 8914501. DOI: 10.1155/2020/8914501. [CrossRef] [Google Scholar]
  11. Liu, C. C., Lin, T. C., Yuan, K. Y., & Chiueh, P. T. (2022). Spatio-temporal prediction and factor identification of urban air quality using support vector machine. Urban Climate, 41, 101055. DOI: 10.1016/j.uclim.2021.101055. [CrossRef] [Google Scholar]
  12. Zhuang, Y. C., & Li, W. (2020). Air quality prediction based on PSO-optimized extreme learning machine neural network. Journal of Shenyang University of Technology, 02, 213-217. [Google Scholar]
  13. Xing, G., Sun, S., & Guo, J. (2020). A new decomposition ensemble learning approach with intelligent optimization for PM2. 5 concentration forecasting. Discrete Dynamics in Nature and Society, 2020(1), 6019826. DOI: 10.1155/2020/6019826. [Google Scholar]
  14. Feng, R., Zheng, H. J., Gao, H., Zhang, A. R., Huang, C., Zhang, J. X.,. .. & Fan, J. R. (2019). Recurrent Neural Network and random forest for analysis and accurate forecast of atmospheric pollutants: a case study in Hangzhou, China. Journal of cleaner production, 231, 1005-1015. DOI: 10.1016/j.jclepro.2019.05.319. [CrossRef] [Google Scholar]
  15. Wu, X., Zhang, C., Zhu, J., & Zhang, X. (2022). Research on PM2. 5 concentration prediction based on the CE-AGA-LSTM model. Applied Sciences, 12(14), 7009. DOI: 10.3390/app12147009. [CrossRef] [Google Scholar]
  16. Zhang, J., Peng, Y., Ren, B., & Li, T. (2021). Pm2. 5 concentration prediction based on cnn-bilstm and attention mechanism. Algorithms, 14(7), 208. DOI: 10.3390/a14070208. [CrossRef] [Google Scholar]
  17. Wei, Y., Zhao, Y., Lu, C., Wei, S., Liu, L., Zhu, Z., & Yan, S. (2016). Cross-modal retrieval with CNN visual features: A new baseline. IEEE transactions on cybernetics, 47(2), 449-460. DOI: 10.1109/TCYB.2016.2519449. [Google Scholar]
  18. Wang, H. B., Chen, Y., Li, J., Liu, W. J. & Li, L. X. (2020). Prediction model of benzene concentration in air based on CNN. Journal of Inner Mongolia University of Technology(Natural Science Edition), 04, 279-285. DOI: 10.13785/j.cnki.nmggydxxbzrkxb.2020.04.006. [Google Scholar]
  19. Huang, C. J., & Kuo, P. H. (2018). A deep CNN- LSTM model for particulate matter (PM2.5) forecasting in smart cities. Sensors, 18(7), 2020. DOI: 10.3390/s18072220. [Google Scholar]
  20. Liu, Y. Y., & Cao, Y. F. (2022). Air quality visualization platform with CNN-LSTM prediction model. Information Technology and Informatization, 04, 19-22. [Google Scholar]
  21. Liu, Y., Pei, L. L., & Hao, X. L. (2022). Air quality index prediction based on WOA-BiLSTM model. Computer Systems & Applications, 10, 389-396. DOI: 10.15888/j.cnki.csa.008725. [Google Scholar]
  22. Yu, C. H., & Liu, L. (2023). seq2seq model based on attention mechanism in PM_(2.5) concentration prediction. Journal of Geomatics, 04, 126-131. DOI: 10.14188/j.2095-6045.2020407. [Google Scholar]
  23. Kareem, S., Hamad, Z. J., & Askar, S. (2021). An evaluation of CNN and ANN in prediction weather forecasting: A review. Sustainable Engineering and Innovation, 3(2), 148-159. DOI: 10.37868/sei.v3i2.id146. [CrossRef] [Google Scholar]
  24. Zhao, X., Song, M., Liu, A., Wang, Y., Wang, T., & Cao, J. (2020). Data-driven temporal-spatial model for the prediction of AQI in Nanjing. Journal of Artificial Intelligence and Soft Computing Research, 10(4), 255-270. [CrossRef] [Google Scholar]
  25. Sarkar, N., Gupta, R., Keserwani, P. K., & Govil, M. C. (2022). Air quality index prediction using an effective hybrid deep learning model. Environmental Pollution, 315, 120404. [CrossRef] [Google Scholar]
  26. Wang, J., Li, X., Jin, L., Li, J., Sun, Q., & Wang, H. (2022). An air quality index prediction model based on CNN-ILSTM. Scientific Reports, 12(1), 8373. [CrossRef] [Google Scholar]
  27. Sardá-Espinosa, A. (2017). Comparing time-series clustering algorithms in r using the dtwclust package. R package vignette, 12, 41. [Google Scholar]
  28. Paparrizos, J., & Gravano, L. (2015). k-shape: Efficient and accurate clustering of time series. In Proceedings of the 2015 ACM SIGMOD international conference on management of data (pp. 1855-1870). DOI: 10.1145/2723372.2737793. [Google Scholar]

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