Machine Learning Based Air quality Assessment of Jabalpur city Madhya Pradesh India

Author(s)	Ms. Priya Mishra, Prof. Dr. D.C. Rahi
Country	India
Abstract	Air pollution is a major environmental and public health concern, making accurate air quality assessment and prediction essential. This study evaluates the ambient air quality of Jabalpur, Madhya Pradesh, using data from five CAAQMS stations: Govind Bhavan Colony, Gupteshwar, Marhatal, Suhagi, and Vijay Nagar. Major pollutants (PM₁₀, PM₂. ₅, SO₂, NO₂, CO, NH₃, and O₃) were analyzed for 2024–2026 using descriptive statistics and CPCB-based Air Quality Index (AQI) calculations. Six machine learning algorithms in WEKA—Gaussian Process, Linear Regression, IBk, M5Rules, M5P Model Tree, and Random Tree—were evaluated using CC, MAE, and RMSE. The M5P Model Tree achieved the best performance, with a CC of 0.9794, MAE of 3.4989, and RMSE of 5.1670. PM₁₀ and PM₂. ₅ were the dominant pollutants across the monitoring stations, while winter (November–February) recorded the highest AQI due to atmospheric inversion, low wind speed, and reduced pollutant dispersion. The findings demonstrate the potential of machine learning for reliable AQI prediction and effective air quality management in Jabalpur
Keywords	Air Pollution, Air Quality Index (AQI), PM₁₀, PM₂.₅, CAAQMS, Machine Learning, M5P Model Tree
Field	Engineering
Published In	Volume 17, Issue 9, September 2026
Published On	2026-09-07

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