Enhancing Air Quality Forecasting Through Deep Learning and Continuous Wavelet Transform

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. We compare our approach with traditional one-dimensional signal processing models. The results show that our 2D pipeline, employing the Morlet mother wavelet, outperforms the baselines significantly. The localized time-frequency representations obtained through CWT highlight hidden dynamics and relationships within the parameter behavior and external factors, leading to more accurate predictions. Overall, our approach demonstrates the potential to advance air quality forecasting and environmental management for healthier living environments worldwide.