SUPERVISED PREDICTIVE MODELING FOR URBAN AIR QUALITY AND EMISSIONS: A COMPARATIVE STUDY OF MACHINE LEARNING AND HYBRID TIME SERIES APPROACHES

Resumen

Air pollution from traffic and industry poses a global health hazard. The problem is that traditional statistical models are inadequate for calculating how these pollutants disperse in such densely populated cities, since the atmosphere follows chaotic and highly complex patterns. This research focuses on evaluating and comparing the accuracy of two predictive approaches: the Random Forest Regressor algorithm and Long Short-Term Memory (LSTM) neural networks, applied to predict PM2.5 and NO2 concentrations. An empirical and quantitative design was chosen. Given the temporary inability to connect to the UCI’s original API, the issue was resolved by using a synthetic database, structured based on Beijing’s historical emissions records, thereby ensuring a controlled simulation environment. During the preprocessing phase, missing records were imputed using linear interpolation, values were normalized using Z-scoring, and time variables were adjusted using cyclic encoding. The results confirm that the LSTM model far outperforms the traditional static alternative. During the evaluations, the recurrent network recorded a root mean square error (RMSE) of 8.77 µg/m³ (MAE: 7.00, R²: 0.72) for PM2.5 and an RMSE of 5.33 µg/m³ (MAE: 4.25, R²: 0.64) for NO₂. These figures represent a reduction in the margin of error of 55.9% and 47.8%, respectively, compared to Random Forest. This demonstrates that LSTM gate architectures possess an outstanding ability to retain long-term temporal dependencies, a crucial factor in anticipating environmental alert peaks. This finding provides a solid basis for integrating these algorithms into Internet of Things (IoT)-based smart city platforms, thereby optimizing government decision-making regarding environmental management.