Open Access
Issue |
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
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|
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Article Number | 01070 | |
Number of page(s) | 18 | |
DOI | https://doi.org/10.1051/epjconf/202532801070 | |
Published online | 18 June 2025 |
- Manisalidis, I., Stavropoulou, E., Stavropoulos, A., & Bezirtzoglou, E. (2020). Environmental and Health Impacts of Air Pollution: A Review. Frontiers in Public Health, 8, 14. doi:10.3389/fpubh.2020.00014 [CrossRef] [PubMed] [Google Scholar]
- World Health Organization. (2018). Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease. WHO. [Google Scholar]
- Box, G.E.P., & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day. [Google Scholar]
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539 [CrossRef] [PubMed] [Google Scholar]
- Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735 [CrossRef] [Google Scholar]
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Available at: https ://www. deeplearningbook. org/ [Google Scholar]
- Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). doi:10.1145/2939672.2939785 [Google Scholar]
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929-1958. [Google Scholar]
- Friedman, J.H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189-1232. [CrossRef] [Google Scholar]
- Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K.Q. (2018). Densely Connected Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4700-4708). DOI: 10.1109/CVPR.2017.243 [Google Scholar]
- Li, Q., & Zhao, D. (2019). Short-Term Air Quality Prediction Using LSTM Neural Networks. IEEE Access, 7, 133293-133302. doi:10.1109/ACCESS.2019.2932984 [Google Scholar]
- Zhang, L., & Ding, R. (2023). Deep Learning Based Multimodal Urban Air Quality Prediction and Its Spatiotemporal Correlation Analysis. Scientific Reports. doi:10.1038/s41598-023-49296-7 [Google Scholar]
- Chen, Y., et. al. (2019). Deep Learning Approaches for Air Quality Prediction. AI & Society. [Google Scholar]
- Wang, X., & Zhao, L. (2020). Comparative Study of Regression Models for AQI Forecasting. Environmental Modelling. [Google Scholar]
- Fang, C., et. al. (2019). An Explainable AI Approach for Air Quality Forecasting. Environmental Modelling & Software, 119, 103-112. [Google Scholar]
- Iqbal, A., Sarkar, P., & Mukherjee, N. (2024). Comparative Analysis of Machine Learning Models for AQI Prediction in Indian Metro Cities. International Journal of Engineering Research & Technology, 13(12), IJERTV13IS120014. doi:10.17577/IJERTV13IS120014 [Google Scholar]
- Rahman, M.M., Hussain, M.E., Nayeem, M.S., Tanha, K.A., Alam, M.S., Uddin, K.M.M., & Babu, H.M.H. (2024). AirNet: Predictive Machine Learning Model for Air Quality Forecasting Using Web Interface. Environmental Systems Research, 13, Article 44. doi:10.1186/s40068-024-00378-z [CrossRef] [Google Scholar]
- Ahmad, S., & Ahmad, T. (2023). AQI Prediction Using Layer Recurrent Neural Network Model: A New Approach. Environmental Monitoring and Assessment, 195, Article 1180. doi:10.1007/s10661-023-11646-3 [PubMed] [Google Scholar]
- Ioannou, I., Nagaradjane, P., Khalifeh, A., Vassiliou, V., et. al. (2025). An Accurate Two-Stage Deep Machine Learning Aided Air Quality Estimation Based on Multiple Gases from Aerial Images. Air Quality, Atmosphere & Health. doi:10.1007/s11869-025-01710-x [Google Scholar]
- Pande, C.B.P., Radhadevi, L., & Satyanarayana, M.B. (2024). Evaluation of Machine Learning and Deep Learning Models for Daily Air Quality Index Prediction in Delhi City, India. Environmental Monitoring and Assessment, 196, Article 1215. doi:10.1007/s10661-024-13351-1 [CrossRef] [Google Scholar]
- Liao, Q., Zhu, M., Wu, L., Pan, X., Tang, X., & Wang, Z. (2020). Deep Learning for Air Quality Forecasts: A Review. Current Pollution Reports, 6, 399-409. doi:10.1007/s40726-020-00159-z [CrossRef] [Google Scholar]
- Zhan, C., Jiang, W., Lin, F., Zhang, S., & Li, B. (2022). A Decomposition-Ensemble Broad Learning System for AQI Forecasting. Neural Computing and Applications, 34, 18461-18472. doi:10.1007/s00521-022-07448-2 [CrossRef] [Google Scholar]
- Wu, Z., Zhao, W., & Lv, Y. (2022). An Ensemble LSTM-Based AQI Forecasting Model with Decomposition-Reconstruction Technique via CEEMDAN and Fuzzy Entropy. Air Quality, Atmosphere & Health, 15, 2299-2311. doi:10.1007/s11869-022-01252-6 [CrossRef] [PubMed] [Google Scholar]
- Kumar, S., Mishra, A., Pandey, H.K., Tiwari, U.K., & Harnisha, M. (2025). Air Quality Prediction Using Ensemble Learning. In Communications in Computer and Information Science (CCIS, Volume 2317, pp. 60-72). doi:10.1007/978-3-031-79041-65 [CrossRef] [Google Scholar]
- Sachdeva, S., Kaur, R., Kimmi, H., Singh, K., Aggarwal, K., & Kharb, S. (2024). Meteorological AQI and Pollutants Concentration-Based AQI Predictor. International Journal of Environmental Science and Technology, 21, 4979-4996. doi:10.1007/s13762-023-05307-8 [CrossRef] [Google Scholar]
- Ansari, A., & Quaff, A.R. (2025). Data-Driven Analysis and Predictive Modelling of Hourly Air Quality Index (AQI) Using Deep Learning Techniques: A Case Study of Azamgarh, India. Theoretical and Applied Climatology, 156, Article 74. DOI: 10.1007/s00704-024-05304-y [CrossRef] [Google Scholar]
- Liu, Q., Cui, B., & Liu, Z. (2024). Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling. Atmosphere, 15(5), 553. doi:10.3390/atmos1505 [CrossRef] [Google Scholar]
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