Empowering traditional ensemble learning through feature learning and wavelet transforms for environmental analysis

The rapid expansion of urbanization and industrial activities has significantly increased atmospheric pollutants, posing critical risks to environmental sustainability and public health. To mitigate this issue, innovative and accurate air quality forecasting tools are essential to enable effective pollution monitoring and management. This study presents SR-ViT-FEL, an innovative deep-learning-based framework designed to enhance air quality forecasting by accurately predicting daily pollutant levels, such as carbon monoxide, by concurrently analyzing different environmental factors. The approach integrates time and frequency domain analyses via Continuous Wavelet Transform and employs a novel ensemble learning strategy that integrates multi-level features extracted from both convolutional and transformer-based architectures. SR-ViT-FEL achieves superior predictive accuracy and adaptability when compared to various traditional monitoring settings. The findings indicate that SR-ViT-FEL not only improves predictive performance but also offers scalability for broader air quality monitoring applications, potentially reducing costs by accurately estimating multiple air quality parameters with fewer physical sensors.