A Review : Air Pollution Prediction using Machine Learning Techniques

Abstract

Air pollution poses a critical threat to public health and the environment, necessitating accurate prediction methods for effective mitigation strategies. This paper explores the application of machine learning techniques in predicting air pollution levels, aiming to improve forecasting accuracy and enable proactive interventions. By leveraging diverse data sources, including satellite imagery, weather forecasts, and socioeconomic factors, machine learning models can capture complex relationships between environmental variables and pollutant concentrations. Regression models, neural networks, and ensemble methods are investigated for their effectiveness in air pollution prediction, considering factors such as feature selection, model evaluation metrics, and real-time data integration. Case studies highlight successful applications of machine learning in air quality prediction, demonstrating the potential for scalable and accessible monitoring systems. The findings underscore the importance of continued research in this field to address emerging challenges and advance environmental management practices. By harnessing the power of data-driven insights, we can create healthier and more sustainable communities for current and future generations.