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

• Author(s): Shreya Lakhmani; Anshika Gupta; Anurag Upadhyay
• Paper ID: 1712948
• Page: 1494-1497
• Published Date: 20-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 6 December-2025

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

Citations

IRE Journals:
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

IEEE:
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, 9(6) https://doi.org/10.64388/IREV9I6-1712948