Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas

Abstract

Air quality (AQ) is one of the most important urban environment indicators for the quality of life. The paper proposes a software solution for predicting and forecasting the air quality index (AQI) in urban areas. The study integrates pollutant factors (CO, NO2, SO2, PM2.5), meteorological parameters (temperature, humidity, wind speed), and traffic data to determine air quality. For this purpose, 19 predictive models were developed and compared: 12 machine learning algorithms, 7 deep learning, and 1 forecasting model based on structural component analysis. The Random Forest Regression model, customized within the study, achieved the best results, with an R2 score of 99.59%, an MAE of 0.22%, an MAPE of 0.68%, and an OP (Overall Precision) score of 95.61%. It was subsequently validated on unseen data and recorded a mean deviation of 0.58%. For short-term AQI forecasting (5 days), the AQIF model achieved an R2 of 71.62%, an MAE of 0.4%, and an MAPE of 0.9%. The proposed solution was integrated into a web application with IoT infrastructure and real-time alert mechanisms. Future directions include expanding the dataset and optimizing hyperparameters for the deep learning models to increase accuracy, as well as integrating PM10 and O3 factors, along with the degree of industrialization and demographic level.

Topics & Concepts