Predicting Air Quality Index using Ensemble Machine Learning

This study focuses on utilizing Ensemble Machine Learning to predict and forecast the Air Quality Index (AQI). The research is motivated by the adverse effects of industrialization and population growth on air quality, leading to detrimental impacts on human health. While numerous methodologies exist for air quality prediction, it is crucial to anticipate future air conditions to minimize their larger consequences. Hence, this study proposes an air quality evaluation system to facilitate future predictions. The study comprises three primary modules: Data Preparation, AQI Forecasting, and Evaluating Air Quality. The Data Preparation Module involves real-time data collection and formatting to ensure compatibility with subsequent modules. In this research, the Sparse Spectrum GPR (SSGPR) method is employed for AQI forecasting, while the cloud model is adopted for air quality evaluation. The study's findings demonstrate the capability of the proposed model to account for the uncertainty and randomness inherent in air quality prediction. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are employed to evaluate the models' effectiveness. Based on the evaluation results, it can be concluded that the Ensemble Machine Learning method utilized in this study effectively predicts and forecasts the Air Quality Index. These predictions play a crucial role in minimizing the adverse impact of air pollution on human health by providing insights into future air conditions. Overall, this research contributes significantly to comprehending and addressing the increasingly urgent challenges associated with air quality.