Deep Gaussian Processes for Air Quality Inference
This work addresses air pollution monitoring, a critical health issue affecting billions, but is incremental as it applies an existing method to a new domain.
The paper tackled the problem of air quality inference for unmonitored locations by applying Deep Gaussian Processes, achieving performance comparable to state-of-the-art models.
Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution. However, AQ station deployment is sparse, and thus air quality inference for unmonitored locations is crucial. Conventional interpolation methods fail to learn the complex AQ phenomena. This work demonstrates that Deep Gaussian Process models (DGPs) are a promising model for the task of AQ inference. We implement Doubly Stochastic Variational Inference, a DGP algorithm, and show that it performs comparably to the state-of-the-art models.