Deep Learning for Air Quality Prediction

This paper introduces Deep Classifier Kriging (DCK), a novel deep learning framework for probabilistic spatial prediction of the Air Quality Index (AQI). It addresses the limitations of traditional methods like kriging, which struggle with the non-Gaussian and nonlinear nature of AQI data. The proposed DCK framework offers improved predictive accuracy and uncertainty quantification, especially when integrating heterogeneous data sources. This is significant because accurate AQI prediction is crucial for regulatory decision-making and public health.