Comparative Analysis Study for Air Quality Prediction in Smart Cities Using Regression Techniques

In smart cities, air pollution has detrimental impacts on human physical health and the quality of living environment. Therefore, correctly predicting air quality plays an important effective action plan to mitigate air pollution and create healthier and more sustainable environments. Monitoring and predicting air pollution is crucial to empower individuals to make informed decisions that protect their health. This research presents a comprehensive comparative analysis focused on air quality prediction using three distinct regression techniques- Random Forest regression, Linear regression, and Decision Tree regression. The main goal of this study is to discern the most effective model by considering a range of evaluation criteria, including Mean Absolute Error and
$R^{2}$
measures. Moreover, it considers the crucial aspects of minimizing prediction errors and enhancing computational efficiency by evaluating the regression models within two frameworks. The findings of this study underscore the superiority of the Decision Tree regression approach over the other models, demonstrating its exceptional accuracy with a high
$R^{2}$
score and a minimal error rate. Moreover, integrating cloud computing technology has resulted in substantial improvements in the execution time of these approaches. This technology enhancement significantly affects the overall efficiency of the air quality prediction process. By leveraging distributed computing resources, real-time air quality forecasting becomes feasible, enabling timely decision-making and proactive measures to address air pollution episodes effectively.