Double-Diffusion: Diffusion Conditioned Diffusion Probabilistic Model For Air Quality Prediction
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
Air quality prediction is a challenging forecasting task due to its
spatio-temporal complexity and the inherent dynamics as well as uncertainty.
Most of the current models handle these two challenges by applying Graph Neural
Networks or known physics principles, and quantifying stochasticity through
probabilistic networks like Diffusion models. Nevertheless, finding the right
balancing point between the certainties and uncertainties remains an open
question. Therefore, we propose Double-Diffusion, a novel diffusion
probabilistic model that harnesses the power of known physics to guide air
quality forecasting with stochasticity. To the best of our knowledge, while
precedents have been made of using conditional diffusion models to predict air
pollution, this is the first attempt to use physics as a conditional generative
approach for air quality prediction. Along with a sampling strategy adopted
from image restoration and a new denoiser architecture, Double-Diffusion ranks
first in most evaluation scenarios across two real-life datasets compared with
other probabilistic models, it also cuts inference time by 50% to 30% while
enjoying an increase between 3-12% in Continuous Ranked Probabilistic Score
(CRPS).
