Enhancing Air Quality Forecasting Through Deep Learning and Continuous Wavelet Transform

Pietro Manganelli Conforti, Andrea Fanti, Pietro Nardelli, Paolo Russo

Abstract: Air quality forecasting plays a crucial role in environmental management and public health. In this paper, we propose a novel approach that combines deep learning techniques with the Continuous Wavelet Transform (CWT) for air quality forecasting based on sensor data. The proposed methodology is agnostic to the target pollutant and can be applied to estimate any available pollutant without loss of generality. The pipeline consists of two main steps: the generation of stacked samples from raw sensor signals using CWT, and the prediction through a custom deep neural network based on the ResNet18 architecture.