• Nandyala Sruthi
• Dr.M.Jayanthi

Air pollution poses a significant threat to public health and the overall quality of life in smart cities. Accurate air quality prediction is essential for developing effective strategies to combat pollution and promote sustainable urban living. This study aims to empower individuals and policymakers by providing insights through precise forecasting of air quality levels. We conduct a detailed comparative analysis of three regression models Random Forest, Linear Regression, and Decision Tree Regression to identify the most efficient approach. The evaluation is based on key performance indicators such as Mean Absolute Error and the R² score. Special attention is given to reducing prediction errors and optimizing computational performance by testing the models in two different operational frameworks. Results highlight that the Decision Tree Regression model delivers superior accuracy, achieving a high R² value with minimal error. Furthermore, the integration of cloud computing significantly enhances processing speed and scalability, making real-time air quality prediction both practical and efficient. This technological advancement enables quicker responses and informed actions during pollution surges, contributing to healthier and more responsive urban environments.

Air Quality Forecasting in Smart Cities Using Machine Learning Regression Models

"Air Quality Forecasting in Smart Cities Using Machine Learning Regression Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.658-663, May-2025, Available :http://www.jetir.org/papers/JETIRGV06093.pdf

"Air Quality Forecasting in Smart Cities Using Machine Learning Regression Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. pp658-663, May-2025, Available at : http://www.jetir.org/papers/JETIRGV06093.pdf