• Jaladi Naga Sireesha
• Dr. Jithendra P R Nayak
• Dr. Rajaram

Air pollution as well as poor ventilation is given by the quick rise of urbanization and industries. It is a “silent public health emergency” due to its damaging consequences on equally people and the environment. perfect air pollution forecast is essential for stakeholders for taking the essential action in order to address this worldwide concern. Compared to other methods, deep learning-based models for prediction have shown promise in the past few years for precise and effective air excellence forecasting. In order to anticipate five air pollutants, including nitrogen dioxide (NO 2), ozone (O3), sulfur dioxide (SO 2), and long short term memory (LSTM) and particulate matter (PM 2.5 as well as PM 10) we conducted a comparison examination of several deep learning-powered single-step forecasting techniques in this research. We utilized a publically accessible dataset obtained via Kaggle called "Air Quality Data in India (2015 – 2020)" for our empirical analysis. It calculates the amount of air pollution present. Three indicators of performance are used to assess the effectiveness of forecasting models: R-squared (R2), mean absolute error (MAE), and root mean square error (RMSE) by a score of 0.59, the outcome demonstrates that machine learning models consistently produced the lowest RMSE when weighed against statistical models. Furthermore, it is discovered that the deep learning model has the greatest R2 score of 0.856.

Deep learning models mean absolute error (MAE), root mean square error (RSME), air pollution.

"A UNIQUE ALGORITHM FOR CONVENTIONAL STATISTICAL MODELS WITH DEEP LEARNING FOR PREDICTION OF AIR QUALITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.888-896, July-2025, Available :http://www.jetir.org/papers/JETIRGX06168.pdf

"A UNIQUE ALGORITHM FOR CONVENTIONAL STATISTICAL MODELS WITH DEEP LEARNING FOR PREDICTION OF AIR QUALITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp888-896, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06168.pdf