Deep Learning for Air Quality Prediction
Published:Dec 29, 2025 13:58
•1 min read
•ArXiv
Analysis
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.
Key Takeaways
- •Proposes Deep Classifier Kriging (DCK), a new deep learning framework for spatial prediction of AQI.
- •Addresses limitations of traditional methods like kriging by handling non-Gaussian and nonlinear data.
- •Offers improved predictive accuracy and uncertainty quantification.
- •Includes a data fusion mechanism for integrating heterogeneous data sources.
- •Supports downstream tasks like exceedance and extreme-event probability estimation for regulatory risk assessment.
Reference
“DCK consistently outperforms conventional approaches in predictive accuracy and uncertainty quantification.”