Predicting Air Quality Index Based on Rainfall Patterns: A Machine Learning Approach with Mathematical Modelling

This study investigates the relationship between rainfall patterns and air quality index (AQI) in the Indian subcontinent using machine learning techniques. We developed a predictive model that incorporates rainfall data, including intensity, duration, and frequency, to forecast AQI values. Methodologically, rainfall and AQI data have been collected and preprocessed from various sources, including weather stations and air quality monitoring stations. These data were used to train and test the machine learning model, which was optimized using various techniques such as feature engineering and hyperparameter tuning. The model's performance was evaluated using several metrics, including absolute mean error, root mean square error, and coefficient of determination. The proposed model demonstrated high accuracy in predicting AQI values, outperforming traditional statistical models. Our findings demonstrate that the predictive model can accurately forecast AQI values up to three days in advance, offering valuable insights for air quality management and policymaking, while also highlighting the significant influence of rainfall patterns, where heavy rainfall events improve air quality and dry periods lead to deterioration. The study underscores the critical role of machine learning-based models in environmental monitoring and prediction, suggesting that accurate AQI forecasts not only advance research in this field but also have vital implications for public health by helping mitigate the adverse effects of air pollution on human health.