Time Series?Based Forecasting of Ground-Level Ozone Concentration Using Machine Learning and Deep Learning Models

Shreya Lakhmani; Anshika Gupta; Anurag Upadhyay

doi:10.64388/IREV9I6-1712948

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Time Series?Based Forecasting of Ground-Level Ozone Concentration Using Machine Learning and Deep Learning Models

Shreya Lakhmani Anshika Gupta Anurag Upadhyay

Subject area: Science,Engineering and Technology · Area of research: Computer Science

DOI: 10.64388/IREV9I6-1712948

Abstract

Ozone pollution poses serious environmental and health challenges, especially in urban regions. Accurate forecasting of ozone concentration levels enables early warning systems and supports policy-level decision-making. This final-year project focuses on ozone level forecasting using time series analysis techniques relevant to data analytics applications. Historical ozone concentration data were analyzed to identify trends, seasonality, and temporal dependencies. The Autoregressive Integrated Moving Average (ARIMA) model was implemented for prediction. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results demonstrate that time series models are effective for short-term ozone forecasting and are suitable for real-world environmental analytics applications.

Keywords

Ozone forecasting, Time series analysis, ARIMA, Data analytics, Air pollution, LSTM, GRU

References

[1] Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley.

[2] Kumar, A., & Jain, S. (2020). Forecasting air quality parameters using time series models. Environmental Monitoring and Assessment, 192(3), 1–12.

[3] Sharma, R., Verma, P., & Singh, A. (2022). Comparative analysis of air pollution forecasting techniques. International Journal of Environmental Science, 14(2), 85–94.

[4] Hochreiter, S., & Schmidhuber, J., Long Short-Term Memory, Neural Computation, MIT Press.

[5] Box, G. E. P., Jenkins, G. M., Reinsel, G. C., Time Series Analysis: Forecasting and Control, Wiley, 5th Edition.

[6] Chawla, N. V. et al., SMOTE: Synthetic Minority Over-sampling Technique, Journal of Artificial Intelligence Research.

[7] Taylor, S. J., & Letham, B., Forecasting at Scale, PeerJ Computer Science.

[8] Zhang, Y. et al., Deep Learning for Air Quality Prediction, Environmental Modelling & Software.

How to cite this paper

Shreya Lakhmani, Anshika Gupta, Anurag Upadhyay "Time Series?Based Forecasting of Ground-Level Ozone Concentration Using Machine Learning and Deep Learning Models" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 1494-1497 https://doi.org/10.64388/IREV9I6-1712948

Shreya Lakhmani, Anshika Gupta, Anurag Upadhyay "Time Series?Based Forecasting of Ground-Level Ozone Concentration Using Machine Learning and Deep Learning Models" Iconic Research And Engineering Journals, vol. 9, no. 6, Dec. 2025, doi: https://doi.org/10.64388/IREV9I6-1712948

Shreya Lakhmani, Anshika Gupta, Anurag Upadhyay (2025). Time Series?Based Forecasting of Ground-Level Ozone Concentration Using Machine Learning and Deep Learning Models. Iconic Research And Engineering Journals, 9(6). doi: https://doi.org/10.64388/IREV9I6-1712948

Shreya Lakhmani, Anshika Gupta, Anurag Upadhyay "Time Series?Based Forecasting of Ground-Level Ozone Concentration Using Machine Learning and Deep Learning Models" Iconic Research And Engineering Journals, vol. 9, no. 6, Dec. 2025. Crossref, https://doi.org/10.64388/IREV9I6-1712948

@article{1712948,
      author = {Shreya Lakhmani, Anshika Gupta, Anurag Upadhyay},
      title = {Time Series?Based Forecasting of Ground-Level Ozone Concentration Using Machine Learning and Deep Learning Models},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {6},
      pages = {1494-1497},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712948.pdf},
      abstract = {Ozone pollution poses serious environmental and health challenges, especially in urban regions. Accurate forecasting of ozone concentration levels enables early warning systems and supports policy-level decision-making. This final-year project focuses on ozone level forecasting using time series analysis techniques relevant to data analytics applications. Historical ozone concentration data were analyzed to identify trends, seasonality, and temporal dependencies. The Autoregressive Integrated Moving Average (ARIMA) model was implemented for prediction. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results demonstrate that time series models are effective for short-term ozone forecasting and are suitable for real-world environmental analytics applications.},
      keywords = {Ozone forecasting, Time series analysis, ARIMA, Data analytics, Air pollution, LSTM, GRU},
      month = {December},
      doi = {https://doi.org/10.64388/IREV9I6-1712948}
  }