Comparative Investigation of Machine Learning and Deep Learning Approaches for Air Quality Prediction

Air pollution is a critical environmental issue with significant impacts on human health and ecosystems, exacerbated by urbanization and industrialization, leading to increased emissions. Forecasting air quality accurately is crucial for risk mitigation and policy direction. Recent advancements in deep learning have enhanced prediction capabilities by automatically extracting features and managing complex data. This paper compares machine learning and deep learning approaches in air quality forecasting, highlighting their strengths and weaknesses. Machine learning offers easier interpretability with limited data but struggles with complex data relationships. Deep learning captures nonlinear patterns more effectively but lacks interpretability and requires more data. Challenges in the field of air quality forecasting include feature selection, model interpretability, and applicability across regions. Future directions involve introducing feedback mechanisms, interpretability methods, and transfer learning to improve model performance and generalization. This review provides valuable insights into existing methodologies and guides future research for effective air quality management.